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  • 51.
    Ek, Johan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Mjukvaruverktyg för loggning och analys av industriella processer2007Independent thesis Basic level (degree of Bachelor)Student thesis
    Abstract [sv]

    This report discusses developing a software log tool for analysis of industrial processes. The target was to develop software that can help electro Engineers for monitor and fault finding in industrial processes. The tool is called PLS (Process log server), and is developed in Visual Studio.NET Framework 2005. PLS works as a client with Beijer Electronics OPC Server. The program is able to read data from PLC (Programmable Logic Controller), trough the OPC Server. PLS connects to all kind of controllers that is supported by the Beijer Electronics OPC Server. Signal data is stored in a database for later analysis. Chosen signals data can easily be exported into a text file. The text file is adopted for import to MS Office Excel. User manual [UM-07] is written as a separate document. The software acted stable through the function test. The final product becomes a first-rate tool that is simple to use. As an advantage, the software can be developed with more functions in the future.

  • 52.
    Eklund, Sven
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Exploring massively parallel models and architectures for efficient computation of evolutionary algorithms2004Doctoral thesis, monograph (Other academic)
  • 53.
    Eklund, Sven
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Time series forecasting using massively parallel genetic programming2003In: Proc. The NIDISC´03 Workshop, Nice, France, 2003Conference paper (Refereed)
  • 54.
    Eklund, Sven
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Fernlund, Hans
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Gonzalez, Avelino
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Genetic context learning – Automatic behavior modeling from observed human performanc2004In: Proceedings of the European Simulation Interoperability Workshop, Edinburgh, 2004Conference paper (Refereed)
  • 55.
    Eklund, Sven
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Fernlund, Hans
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Gonzalez, Avelino
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    The CxBR Diffusion Engine - A tool for modeling human behavior on the battle field2004In: Proceedings of the Third Swedish-American Workshop on Modeling and Simulation, SAWMAS, Coca Beach, Florida, 2004Conference paper (Refereed)
  • 56.
    Elf, Marie
    et al.
    Chalmers Tekniska Högskola.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    The role for simulation in the design of new health care environments2003In: The 8th International Congress in Nursing Informatics - NI2003, Rio de Janeiro, Brazil, 2003Conference paper (Refereed)
  • 57.
    Elgeholm, Josef
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    XML-mottagning av trafikinformation2003Independent thesis Basic level (degree of Bachelor)Student thesis
    Abstract [sv]

    Trafiq används av Columna för att distribuera trafikinformation. Funktionen är först och främst att förädla och förmedla information. En viktig del i denna tjänst är kopplingen mot Vägverkets (VV) tjänst Triss som förser Trafiq med trafikinformation. Överföringen av information från VV till Columna sker idag med filer och FTP. VV tillhandahåller numera en tjänst där data skickas på XML-format med http. Min uppgift var att implementera mottagaren i .NET och C# på Columna. I utredningen utreds de mekanismer som ligger till grund för Internettjänster och distribuerade funktioner över Internet. Min slutsats är att http och webbservrar är ett kraftfullt verktyg och kan användas för att lösa många problem som har med datorkommunikation att göra.

  • 58.
    Eriksson, Christoffer
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Karlsson, Andreas
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Konstruktion av en Marknadsplats i .NET för Högskolan Dalarna2003Independent thesis Basic level (degree of Bachelor)Student thesis
    Abstract [sv]

    Vi har i vårat examensarbete tagit fram en e-marknadsplats i för Högskolan Dalarna. Marknadsplatsen är programmerad i asp.NET och vb.NET. Fram tills idag har skolans anslagstavlor använts flitigt för annonsering av, inte bara studentlitteratur, utan allt från pennor till bilar. Högskolan Dalarna har därför en önskan om att få en e-marknadsplats där studenter har möjlighet att annonsera.

  • 59.
    Eriksson, Mikael
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Schemaläggningsalgoritm för arbetstidsschema2006Independent thesis Basic level (degree of Bachelor)Student thesis
    Abstract [sv]

    Programmet schemaläggaren har funnits sedan 1994, och används på arbetsplatser för att på ett strukturerat sätt lägga scheman för de anställda. Programmet har sedan starten varit sådant att man själv ska skapa alla scheman och även koppla dessa till de anställda, men det finns en önskan att programmet ska vara mer användarvänligt och lättanvänt. Ett steg i utvecklingen är att skapa en automatisk schemaläggningsguide som gör det mesta av jobbet åt användaren. Etex AB, som detta examensarbete är gjort åt, avser att med detta examensarbete skapa en prototyp på en sådan schemaläggningsguide som ska implementeras i det befintliga programmet. Denna guide ska alltså skapa scheman utifrån given data och sedan koppla dessa scheman till de anställda som valts. Allt detta ska ske automatiskt. Efter detta har man sedan möjlighet att antingen spara de scheman och kopplingar som gjorts, eller avvisa dem. Grundläggande tanken med denna modul är att den ska passa in i det befintliga programmet utan att vara bundet till det, så man ska kunna överföra den till en annan applikation om nödvändigt. Trots vissa komplikationer efter vägen har arbetet ändå slutförts och med fortsatt arbete kan modulen bli en bra del av programmet Schemaläggaren.

  • 60.
    Eriksson, Robin
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Forensisk undersökning av tränings-/aktivitetsklocka: Forensisk undersökning av Polar M400 löparklocka samttillhörande mobilapplikation PolarFlow.2017Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This bachelor thesis look into the possibilities for conducting a forensicexamination of a so-called running-/training watch, which in this case is a PolarM400 unit. Most of these types of products have an associated mobile applicationthat the device can use for synchronization, whereupon the collected data ispresented graphically and comprehensively for the user. Polar uses the mobileapplication called PolarFlow. The purpose of the work has been to investigate howto extract the information and content of the product and what sort of data thatactually is stored that could be of forensic interest. The main focus has been basedon information from the device itself, the Polar M400, but the work also containsan investigation of the associated PolarFlow mobileapplication which provednecessary to more accurately map and understand the data retrieved directly fromthe device. To investigate and map the content the device methodically has beenprepared with information in different steps, after which the filesystem has beenextracted and analyzed. The result shows that much of the prepared informationand other artifacts are to be found on the product together with the associatedmobile application. However a large part of the data found needs to be processedto some extent to be understood and used further on, but there is also muchinformation that can be read in "plain text".

  • 61.
    Fan, Ming
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Nordström, Ernst
    Communication research at Dalarna University2005In: Second Swedish National Computer Networking Workshop, Karlstad, 2005Conference paper (Refereed)
  • 62.
    Farooq, Farhan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Optimal Path Searching through Specified Routes using different Algorithms2009Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    To connect different electrical, network and data devices with the minimum cost and shortest path, is a complex job. In huge buildings, where the devices are placed at different locations on different floors and only some specific routes are available to pass the cables and buses, the shortest path search becomes more complex. The aim of this thesis project is, to develop an application which indentifies the best path to connect all objects or devices by following the specific routes. To address the above issue we adopted three algorithms Greedy Algorithm, Simulated Annealing and Exhaustive search and analyzed their results. The given problem is similar to Travelling Salesman Problem. Exhaustive search is a best algorithm to solve this problem as it checks each and every possibility and give the accurate result but it is an impractical solution because of huge time consumption. If no. of objects increased from 12 it takes hours to search the shortest path. Simulated annealing is emerged with some promising results with lower time cost. As of probabilistic nature, Simulated annealing could be non optimal but it gives a near optimal solution in a reasonable duration. Greedy algorithm is not a good choice for this problem. So, simulated annealing is proved best algorithm for this problem. The project has been implemented in C-language which takes input and store output in an Excel Workbook

  • 63.
    Feng, Sitao
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Evaluation of Red Colour Segmentation Algorithms in Traffic Signs Detection2010Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    Colour segmentation is the most commonly used method in road signs detection. Road sign contains several basic colours such as red, yellow, blue and white which depends on countries. The objective of this thesis is to do an evaluation of the four colour segmentation algorithms. Dynamic Threshold Algorithm, A Modification of de la Escalera’s Algorithm, the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm. The processing time and segmentation success rate as criteria are used to compare the performance of the four algorithms. And red colour is selected as the target colour to complete the comparison. All the testing images are selected from the Traffic Signs Database of Dalarna University [1] randomly according to the category. These road sign images are taken from a digital camera mounted in a moving car in Sweden. Experiments show that the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm are more accurate and stable to detect red colour of road signs. And the method could also be used in other colours analysis research. The yellow colour which is chosen to evaluate the performance of the four algorithms can reference Master Thesis of Yumei Liu.

  • 64.
    Feng, Yi
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dynamic Fuzzy Logic Control of Genetic Algorithm Probabilities2008Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    Genetic algorithms are commonly used to solve combinatorial optimization problems. The implementation evolves using genetic operators (crossover, mutation, selection, etc.). Anyway, genetic algorithms like some other methods have parameters (population size, probabilities of crossover and mutation) which need to be tune or chosen. In this paper, our project is based on an existing hybrid genetic algorithm working on the multiprocessor scheduling problem. We propose a hybrid Fuzzy- Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem. The algorithm consists in adding a fuzzy logic controller to control and tune dynamically different parameters (probabilities of crossover and mutation), in an attempt to improve the algorithm performance. For this purpose, we will design a fuzzy logic controller based on fuzzy rules to control the probabilities of crossover and mutation. Compared with the Standard Genetic Algorithm (SGA), the results clearly demonstrate that the FLGA method performs significantly better.

  • 65.
    Feng, Yinda
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Ant colony for TSP2010Independent thesis Basic level (degree of Bachelor)Student thesis
    Abstract [en]

    The aim of this work is to investigate Ant Colony Algorithm for the traveling salesman problem (TSP). Ants of the artificial colony are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP graph. This paper is based on the ideas of ant colony algorithm and analysis the main parameters of the ant colony algorithm. Experimental results for solving TSP problems with ant colony algorithm show great effectiveness.

  • 66. Fernlund, H. K. G.
    et al.
    Gonzalez, A. J.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Georgiopoulos, M.
    DeMara, R. F.
    Learning tactical human behavior through observation of human performance2006In: IEEE transactions on systems, man and cybernetics. Part B. Cybernetics, ISSN 1083-4419, E-ISSN 1941-0492, Vol. 36, no 1, p. 128-140Article in journal (Refereed)
    Abstract [en]

    It is widely accepted that the difficulty and expense involved in acquiring the knowledge behind tactical behaviors has been one limiting factor in the development of simulated agents representing adversaries and teammates in military and game simulations. Several researchers have addressed this problem with varying degrees of success. The problem mostly lies in the fact that tactical knowledge is difficult to elicit and represent through interactive sessions between the model developer and the subject matter expert. This paper describes a novel approach that employs genetic programming in conjunction with context-based reasoning to evolve tactical agents based upon automatic observation of a human performing a mission on a simulator. In this paper, we describe the process used to carry out the learning. A prototype was built to demonstrate feasibility and it is described herein. The prototype was rigorously and extensively tested. The evolved agents exhibited good fidelity to the observed human performance, as well as the capacity to generalize from it.

  • 67.
    Fernlund, Hans
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Evolving Models from Observed Human Performance2004Doctoral thesis, monograph (Other academic)
  • 68.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    A novel fuzzy approach for shape determination of traffic signs2005In: Second Indian International Conference on Arificial Intelligence, Pune, India, 2005Conference paper (Refereed)
    Abstract [en]

    In this paper, a novel fuzzy approach is developed to determine the shape of traffic signs. More than 1600 images of traffic signs were collected in different light conditions by a digital camera mounted in a car and used for testing this approach. Every RGB image was converted into HSV colour space, and segmented by using a set of fuzzy rules depending on the hue and saturation values of each pixel in the HSV colour space. The fuzzy rules are used to extract the colours of the road signs. Objects in each segmented image are labelled and tested for the presence of probable sign. All small objects under certain threshold are discarded, and the remaining objects are tested by a fuzzy shape recognizer which invokes another set of fuzzy rules. Four shape measures are used to decide the shape of the sign; rectangularity, triangularity, ellipticity, and the new shape measure octagonality.

  • 69.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Color detection and segmentation for road and traffic signs2004In: IEEE conference on Cybernetics and Intelligent Systems, Singapore, 2004Conference paper (Refereed)
    Abstract [en]

    This paper aims to present three new methods for color detection and segmentation of road signs. The images are taken by a digital camera mounted in a car. The RGB images are converted into IHLS color space, and new methods are applied to extract the colors of the road signs under consideration. The methods are tested on hundreds of outdoor images in different light conditions, and they show high robustness. This project is part of the research taking place in Dalarna University / Sweden in the field of the ITS.

  • 70.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Color detection and segmentation for road and traffic signs2004In: IEEE Conference on Cybernetics and Intelligent Systems, 2004. / [ed] IEEE, Singapore, 2004, Vol. 2, p. 809-814Conference paper (Refereed)
    Abstract [en]

    This paper aims to present three new methods for color detection and segmentation of road signs.  The images are taken by a digital camera mounted in a car.  The RGB images are converted into IHLS color space, and new methods are applied to extract the colors of the road signs under consideration. The methods are tested on hundreds of outdoor images in different light conditions, and they show high robustness. This project is part of the research taking place in Dalarna University / Sweden in the field of the ITS.

  • 71.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Road and Traffic Sign Color Detection and Segmentation - A Fuzzy Approach2005In: Machine Vision Applications (MVA2005), Tsukuba Science City, 2005Conference paper (Refereed)
    Abstract [en]

    This paper presents a new algorithm for color detection and segmentation of road signs based on fuzzy sets. The images were taken by a digital camera mounted in a car. The RGB image was converted into HSV color space, and segmented by using a set of fuzzy rules depending on the hue and saturation values of each pixel in the HSV color space. The fuzzy rules are used to extract the colors of the road signs. The method was tested on outdoor images in different light conditions, and it was tested on color images from different European countries and it showed high robustness. This project is part of the research conducted by Dalarna University-Sweden and Napier University Edinburgh-Scotland in the field of the Intelligent Transport Systems (ITS).

  • 72.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Segmentation and enhancement of low quality fingerprint images2016In: Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part II, China - Shanghai: Springer, 2016, Vol. 10042, p. 371-384Conference paper (Refereed)
    Abstract [en]

    This paper presents a new approach to segment low quality finger-print images which are collected by low quality fingerprint scanners. Images collected using such readers are easy to collect but difficult to segment. The proposed approach focuses on automatically segment and enhance these fingerprint images to reduce the detection of false minutiae and hence improve the recognition rate. There are four major contributions of this paper. Firstly, segmentation of fingerprint images is achieved via morphological filters to find the largest object in the image which is the foreground of the fingerprint. Secondly, specially designed adaptive thresholding algorithm to deal with fingerprint images. The algorithm tries to fit a curve between the gray levels of the pixels of each row or column in the fingerprint image. The curve represents the binarization threshold of each pixel in the corresponding row or column. Thirdly, noise reduction and ridge enhancement is achieved by invoking a rotational invariant anisotropic diffusion filter. Finally, an adaptive thinning algorithm which is immune against spurs is invoked to generate the recognition ready fingerprint image. Segmentation of 100 images from databases FVC2002 and FVC2004 was performed and the experiments showed that 96 % of images under test are correctly segmented.

  • 73.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Shadow and highlight invariant colour segmentation algorithm for traffic signs2006In: 2006 IEEE Conference on cybernetics and intelligent systems, vol.1 and 2, 2006Conference paper (Refereed)
    Abstract [en]

    Shadows and highlights represent a challenge to the computer vision researchers due to a variance in the brightness on the surfaces of the objects under consideration. This paper presents a new colour detection and segmentation algorithm for road signs in which the effect of shadows and highlights are neglected to get better colour segmentation results. Images are taken by a digital camera mounted in a car. The RGB images are converted into HSV colour space and the shadow-highlight invariant method is applied to extract the colours of the road signs under shadow and highlight conditions. The method is tested on hundreds of outdoor images under such light conditions, and it shows high robustness; more than 95% of correct segmentation is achieved.

  • 74.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Traffic and Road Sign Recognition2008Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers’ tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification. Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera’s algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day. Approximately 97% successful segmentation rate was achieved using this algorithm. Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%. Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim’s shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment’s orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but ?-SVM gives better results in some case.

  • 75.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Traffic Sign detection and recognition2017In: Computer Vision and Imaging in Intelligent Transportation Systems, John Wiley & Sons, 2017, 1, p. 343-374Chapter in book (Refereed)
    Abstract [en]

    This chapter presents an overview of traffic sign detection and recognition. It describes the characteristics of traffic signs and the requirements and difficulties when dealing with traffic sign detection and recognition in outdoor images. The chapter also covers the different techniques invoked to segment traffic signs from the different traffic scenes and the techniques employed for the recognition and classification of traffic signs. It points many problems regarding the stability of the received colour information, variations of these colours with respect to the daylight conditions, and absence of a colour model that can led to a good solution. It also proposes an adaptive colour segmentation model based on Neural Networks. The chapter demonstrates the way to classify segmented traffic signs by employing one of widely used classifiers, AdaBoost , based on a set of features, in this case HOG descriptors, which was developed for pedestrian recognition but found the way for many applications in different fields. The chapter ends by showing examples where traffic sign recognition is applicable in vehicle industry

  • 76.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Traffic sign recognition by fuzzy sets2008In: Intelligent Vehicles Symposium, 2008 IEEE, Eindhoven, 2008Conference paper (Refereed)
    Abstract [en]

    In this paper, a novel fuzzy approach is developed to recognize traffic signs. More than 3400 images of traffic signs were collected in different light conditions by a digital camera mounted in a car and used for developing and testing this approach. Every RGB image was converted into HSV color space and segmented by using a set of fuzzy rules depending on the hue and saturation values of each pixel. Objects in each segmented image are labeled and tested for the presence of probable sign. Objects passed this test are recognized by a fuzzy shape recognizer which invokes another set of fuzzy rules. These fuzzy rules are based on four invariant shape measures which are invoked to decide the shape of the sign; rectangularity, triangularity, ellipticity, and the new shape measure octagonality. The method is tested in different environmental conditions and it shows high robustness.

  • 77.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Traffic Sign Recognition: visions and systems2010Book (Other academic)
  • 78.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Traffic sign recognition without color information2015In: Colour and Visual Computing Symposium (CVCS), 2015 / [ed] Pedersen, M; Thomas, JB, IEEE conference proceedings, 2015, p. 1-6Conference paper (Refereed)
    Abstract [en]

    Color represents an important attribute in the field of traffic sign recognition. However, when the color of the traffic sign fades or the traffic scene is collected in gray as in the case of Infrared imaging, then color based recognition systems fail. Other problems related to color are simply that different countries use different colors. Even within the European Union, colors of traffic signs are not the same. This paper aims to present a new approach to detect traffic signs without color attributes. It is based a two-stage sliding window which detects traffic signs in the multi-scale image. Histogram of Oriented Gradients HOG descriptors are computed as a quality function which are evaluated by two SVM classifier; the coarse and the fine detectors. Different objects detected by the coarse detectors are clustered and a fine search is conducted in the areas where traffic signs are more probable to exist. Experiments conducted to detect traffic signs under different light conditions such as sunny, cloudy, fog and snow fall have showed a performance of 98% and very low false positive rate. The proposed approach was tested on the Yield traffic signs because it has a simple triangular shape which can be found in many places other than the traffic signs which represent a challenge to the proposed approach.

  • 79.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Traffic sign recognition without color information2015Report (Other academic)
    Abstract [en]

    Color represents an important attribute in the field of traffic sign recognition. However, when the color of the traffic sign fades or the traffic scene is collected in gray as in the case of Infrared imaging, then color based recognition systems fail. Other problems related to color are simply that different countries use different colors. Even within the European Union, colors of traffic signs are not the same.

    This paper aims to present a new approach to detect traffic signs without color attributes. It is based a two-stage sliding window which detects traffic signs in the multi-scale image. Histogram of Oriented Gradients (HOG) descriptors are computed as a quality function which are evaluated by two SVM classifier; the coarse and the fine detectors. 

    Different objects detected by the coarse detectors are clustered and a fine search is conducted in the areas where traffic signs are more probable to exist. 

    Experiments conducted to detect traffic signs under different light conditions such as sunny, cloudy, fog and snow fall have showed a performance of 98% and very low false positive rate.  The proposed approach was tested on the Yield traffic signs because it has a simple triangular shape which can be found in many places other than the traffic signs and represent a challenge to the proposed approach.

  • 80.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Traffic Signs Color Detection and Segmentation in Poor Light Conditions2005In: Machine Vision Applications (MVA2005), Tsukuba Science City, 2005Conference paper (Refereed)
    Abstract [en]

    This paper presents a new algorithm for color detection and segmentation of road signs in poor light conditions. The images were taken by a digital camera mounted in a car. The RGB channels of the digital images were enhanced separately by histogram equalization, and then a color constancy algorithm was applied to extract the true colors of the sign. The resultant image was then converted into HSV color space, and segmented to extract the colors of the road signs. The method was tested on outdoor images in different poor light conditions such as fog and snow, and they show high robustness. This project is part of the research taking place at Dalarna University - Sweden in the field of the Intelligent Transport Systems (ITS).

  • 81.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Barsam, Payvar
    Optimization of cable cycles: a trade-off between reliability and cost2015In: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 5, no 2, p. 43-57Article in journal (Refereed)
    Abstract [en]

    This paper elaborates the routing of cable cycle through available routes in a building in order to link a set of devices, in a most reasonable way. Despite of the similarities to other NP-hard routing problems, the only goal is not only to minimize the cost (length of the cycle) but also to increase the reliability of the path (in case of a cable cut) which is assessed by a risk factor. Since there is often a trade-off between the risk and length factors, a criterion for ranking candidates and deciding the most reasonable solution is defined. A set of techniques is proposed to perform an efficient and exact search among candidates. A novel graph is introduced to reduce the search-space, and navigate the search toward feasible and desirable solutions. Moreover, admissible heuristic length estimation helps to early detection of partial cycles which lead to unreasonable solutions. The results show that the method provides solutions which are both technically and financially reasonable. Furthermore, it is proved that the proposed techniques are very efficient in reducing the computational time of the search to a reasonable amount.

  • 82.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Bhuiyan, Nizam
    Biswas, Rubel
    Prohibitory traffic signs detection using LVQ and windowed Hough transform2011In: IICAI-11 (5 th Indian International Conference on Artificial Intelligence), Tumkur, India, 2011Conference paper (Refereed)
    Abstract [en]

    Prohibitory traffic signs represent an important group of traffic signs which are used to prohibit certain types of manoeuvres or some types of traffic. Speed limits signs belong to this group and speed is the main cause of many deadly accidents. Detecting this group in good time may be helpful to avoid many fatal accidents. This paper presents a new approach to detecting prohibitory traffic signs which is based on colour segmentation using LVQ and windowed Hough Transform. Experiments conducted to check the robustness of this approach indicated that 98.5% of the traffic signs invoked for this test were successfully detected. This test was carried out using images collected under a wide range of environmental conditions.

  • 83.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Bin Mumtaz, Al Hasanat
    Adaptive Shadow and Highlight Invariant Colour Segmentation for Traffic Sign Recognition Based on Kohonen SOM2011In: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 20, no 1, p. 15-31Article in journal (Refereed)
    Abstract [en]

    This paper describes an intelligent algorithm for traffic sign recognition which converges quickly, is accurate in its segmentation and adaptive in its behaviour. The proposed approach can segment images of traffic signs in different lighting and environmental conditions and in different countries. It is based on using Kohonen's Self-Organizing Maps (SOM) as a clustering tool and it is developed for Intelligent Vehicle applications. The current approach does not need any prior training. Instead, a slight portion, which is about 1% of the image under investigation, is used for training. This is a key issue to ensure fast convergence and high adaptability. The current approach was tested by using 442 images which were collected under different environmental conditions and from different countries. The proposed approach shows promising results; good improvement of 73% is observed in faded traffic sign images compared with 53.3% using the traditional algorithm. The adaptability of the system is evident from the segmentation of the traffic sign images from various countries where the result is 96% for the nine countries included in the test.

  • 84.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Biswas, Rubel
    Bhuiyan, Nizam
    An adaptive approach to detect warning traffic signs using som and windowed hough transform2011In: IASTED, Krete, 2011, p. 195-202Conference paper (Refereed)
    Abstract [en]

    Warning traffic signs represent an important group of traffic signs which indicate danger for road users. Detecting this group in good time may be helpful to avoid many fatal accidents. This paper presents a new approach to detecting warning traffic signs which is based on color segmentation using Self Organizing Maps and windowed Hough Transform. The proposed system is a standalone and adaptive which means that it works without any kind of training. This is due to the fact that color segmentation using SOM employs 1% of the image under investigation for the training and Hough transform is invoked to detect the shape of this group of traffic signs. Experiments conducted to check the robustness of this approach indicated that 95.6% of the traffic signs invoked for this test were successfully detected. This test was carried out under a wide range of environmental conditions and in different European countries.

  • 85.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Biswas, Rubel
    Davami, Erfan
    Traffic sign detection based on AdaBoost color segmentation and SVM classification2013In: Eurocon 2013: IEEE Conference Publications / [ed] IEEE, 2013, p. 2005-2010Conference paper (Refereed)
    Abstract [en]

    This paper aims to present a new approach to detect traffic signs which is based on color segmentation using AdaBoost binary classifier and circular Hough Transform.The Adaboost classifier was trained to segment traffic signs images according to the desired color. A voting mechanism was invoked to establish a property curve for each of the candidates. SVM classifier was trained to classify the property curves of each object into their corresponding classes.

    Experiments conducted on Adaboost color segmentation under different light conditions such as sunny, cloudy, fog and snow fall have showed a performance of 95%. The proposed system was tested on two different groups of traffic signs; the warning and the prohibitory signs. In the case of warning signs, a recognition rate of 98.4% was achieved while it was 97% for prohibitory traffic signs. This test was carried out under a wide range of environmental conditions.

  • 86.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Davami, Erfan
    Classification with NormalBoost- Case Study Traffic Sign Classification2012In: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 21, no 1, p. 25-43Article in journal (Refereed)
    Abstract [en]

    NormalBoost is a new boosting algorithm which is capable of classifying a multi-dimensional binary class dataset. It adaptively combines several weak classifiers to form a strong classifier. Unlike many boosting algorithms which have high computation and memory complexities, NormalBoost is capable of classification with low complexity. The purpose of this paper is to present NormalBoost as a framework which establishes a platform to solve classification problems. The approach was tested with a dataset which was extracted automatically from real-world traffic sign images. The dataset contains both images of traffic sign borders and speed limit pictograms. This framework involves the computation of Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images which were classified was 6500 binary images. A -fold validation was invoked to check the validity of the classification which resulted in a classification rate of 98.4% and 98.9% being achieved for these two databases. This framework is distinguished by its invariance to in-plane rotation of the images under consideration. Experiments show that the classification rate remains almost constant when traffic sign images with different angles of rotations were tested.

  • 87.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Davami, Erfan
    Eigen Based Traffic Sign Recognition Which Aids In Achieving Intelligent Speed Adaptation2011In: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 20, no 2, p. 129-145Article in journal (Refereed)
    Abstract [en]

    Speed is one of the major factors by which the traffic safety is affected. If the speed limit traffic signs on the road are recognised and displayed to a driver, this will be a motivation to keep the vehicle's speed within the permitted range. The purpose of this paper is to investigate Eigen-based traffic sign recognition which can aid in the development of Intelligent Speed Adaptation. This system is based on invoking the PCA technique to detect the unknown speed limit traffic sign and computes its best effective Eigen vectors. The traffic sign is then recognized and classified by using the shortest Euclidean distance to the different speed limit traffic sign classes. The system was trained using 24 037 images which were collected in different light conditions. To check the robustness of this system, it was tested against 1429 images and it was found that the accuracy of recognition was 97.5% which indicates clearly the high robustness targeted by this system.

  • 88.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Davami, Erfan
    Eigen-based traffic sign recognition2011In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 5, no 3, p. 190-196Article in journal (Refereed)
    Abstract [en]

    This paper’s purpose is to introduce Eigen-based traffic sign recognition. This technique is based on invoking the PCA algorithm to choose the most effective components of traffic sign images to classify an unknown traffic sign. A set of weights are computed from the most effective Eigen vectors of the traffic sign. By using the Euclidean distance, unknown traffic sign images are then classified. The approach was tested on two different databases of traffic sign’s borders and speed limit pictograms which were extracted automatically from real-world images. A classification rate of 96.8% and 97.9% was achieved for these two databases. To check the robustness of this approach, non-traffic sign objects and occluded signs were invoked. A performance of 71% was achieved when occluded signs are used. When signs were rotated 10 degrees around their centre, the performance became 89% when traffic signs’ outer shapes were used and for rotated speed limit pictograms the result was 80%.

  • 89.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Davami, Erfan
    University of Central Florida.
    Multiclass Adaboost Based on an Ensemble of Binary Adaboosts2013In: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 3, no 2, p. 57-70Article in journal (Refereed)
    Abstract [en]

    This paper presents a multi-class AdaBoost based on incorporating an ensemble of binary AdaBoosts which is organized as Binary Decision Tree (BDT). It is proved that binary AdaBoost is extremely successful in producing accurate classification but it does not perform very well for multi-class problems. To avoid this performance degradation, the multi-class problem is divided into a number of binary problems and binary AdaBoost classifiers are invoked to solve these classification problems. This approach is tested with a dataset consisting of 6500 binary images of traffic signs. Haar-like features of these images are computed and the multi-class AdaBoost classifier is invoked to classify them. A classification rate of 96.7% and 95.7% is achieved for the traffic sign boarders and pictograms, respectively. The proposed approach is also evaluated using a number of standard datasets such as Iris, Wine, Yeast, etc. The performance of the proposed BDT classifier is quite high as compared with the state of the art and it converges very fast to a solution which indicates it as a reliable classifier.

  • 90.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Davami, Erfan
    Jomaa, Diala
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Segmentation of fingerprint images based on bi-level processing using fuzzy rules2012In: Fuzzy Information Processing Society (NAFIPS), 2012 Annual Meeting of the North American, 2012, p. 1-6Conference paper (Refereed)
    Abstract [en]

    This paper presents a new approach to segment low quality fingerprint images which are collected by low quality fingerprint readers. Images collected using such readers are easy to collect but difficult to segment. The proposed approach is based on combining global and local processing to achieve segmentation of fingerprint images. On the global level, the fingerprint is located and extracted from the rest of the image by using a global thresholding followed by dilation and edge detection of the largest object in the image. On the local level, fingerprint's foreground and its border image are treated using different fuzzy rules which the two images are segmented. These rules are based on the mean and variance of the block under consideration. The approach is implemented in three stages; preprocessing, segmentation, and post-processing. Segmentation of 100 images was performed and compared with manual examinations by human experts. The experiments showed that 96% of images under test are correctly segmented. The results from the quality of segmentation test revealed that the average error in block segmentation was 2.84% and the false positive and false negatives were approximately 1.4%. This indicates the high robustness of the proposed approach.

  • 91.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Road and traffic sign detection and recognition2005In: 10th EWGT Meeting and 16th Mini-EURO Conference, Poznan, Poland, 2005Conference paper (Refereed)
    Abstract [en]

    This paper presents an overview of the road and traffic sign detection and recognition. It describes the characteristics of the road signs, the requirements and difficulties behind road signs detection and recognition, how to deal with outdoor images, and the different techniques used in the image segmentation based on the colour analysis, shape analysis. It shows also the techniques used for the recognition and classification of the road signs. Although image processing plays a central role in the road signs recognition, especially in colour analysis, but the paper points to many problems regarding the stability of the received information of colours, variations of these colours with respect to the daylight conditions, and absence of a colour model that can led to a good solution. This means that there is a lot of work to be done in the field, and a lot of improvement can be achieved. Neural networks were widely used in the detection and the recognition of the road signs. The majority of the authors used neural networks as a recognizer, and as classifier. Some other techniques such as template matching or classical classifiers were also used. New techniques should be involved to increase the robustness, and to get faster systems for real-time applications.

  • 92.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    SVM based traffic sign classification using legender moments2007In: Proceedings of the 3rd Indian International Conference on Artificial Intelligence, IICAI 2007, 2007, p. 957-968Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel approach to recognize traffic signs using Support Vector Machines (SVMs) and Legendre Moments. Images of traffic signs are collected by a digital camera mounted in a vehicle. They are color segmented and all objects which represent signs are extracted and normalized to 36×36 pixels images. Legendre moments of sign borders and speed-limit signs of 350 and 250 images are computed and the SVM classifier is trained with theses features. Two stages of SVM are trained; the first stage determines the class of the sign from the shape of its border and the second one determines the pictogram of the sign. Training and testing of both SVM classifiers are done offline by using still images. In the online mode, the system loads the SVM training model and performs recognition. Copyright © 2007 IICAI.

  • 93.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    SVM Based Traffic Sign Classification Using Legendre Moments2007In: Third Indian International Conference on Artificial Intelligence, Pune, India, 2007Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel approach to recognise traffic signs using Support Vector Machines (SVMs) and Legendre Moments. Images of traffic signs are collected by a digital camera mounted in a vehicle. They are colour segmented and all objects which represent signs are extracted and normalised to 36x36 pixels images. Legendre moments of sign borders and speed-limit signs of 350 and 250 images are computed and the SVM classifier is trained with theses features. Two stages of SVM are trained; the first stage determines the class of the sign from the shape of its border and the second one determines the pictogram of the sign. Training and testing of both SVM classifiers are done offline by using still images. In the online mode, the system loads the SVM training model and performs recognition.

  • 94.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Traffic sign classification using invariant features and support vector machines2008In: Intelligent Vehicles Symposium, 2008 IEEE, 2008, Vol. 1-3, p. 530-535Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel approach to recognize traffic signs using invariant features and support vector machines (SVM). Images of traffic signs are collected by a digital camera mounted in a vehicle. They are color segmented and all objects which represent signs are extracted and normalized to 36 x 36 pixels images. Invariant features of sign rims and speed-limit sign interiors of 350 and 250 images are computed and the SVM classifier is trained with these features. Two stages of SVM are trained; the first stage determines the shape of sign rim and the second determines the pictogram of the sign. Training and testing of both SVM classifiers are done using still images. The best performance achieved is 98% for sign rims and 93% for speed limit signs.

  • 95.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Aenugula, Dinesh
    Baddam, Sruthi
    Invariant road sign recognition with fuzzy ARTMAP and zernike moments2007In: 2007 IEEE Intelligent Vehicles Symposium, vols 1-3, 2007, Vol. 1-3, p. 1-6Conference paper (Refereed)
    Abstract [en]

    In this paper, a novel approach to recognize road signs is developed. Images of road signs are collected by a digital camera mounted in a vehicle. They are segmented using colour information and all objects which represent signs are extracted, normalized to 36x36 pixels, and used to train a Fuzzy ARTMAP neural network by calculating Zernike moments for these objects as features. Sign borders and pictograms are investigated in this study. Zernike moments of sign borders and speed-limit signs of 210 and 150 images are calculated as features. A fuzzy ARTMAP is trained directly with features, or by using PCA for dimension reduction, or by using LDA algorithm as dimension reduction and data separation algorithm. Two Fuzzy ARTMAP Neural Networks are trained. The first NN determines the class of the sign from the shape of its border and the second one determines the sign itself from its pictogram. Training and testing of both NNs is done offline by using still images. In the online mode, the system loads the Fuzzy ARTMAP Neural Networks, and performs recognition process. An accuracy of about 99% is achieved in sign border recognition and 96% for Speed-Limit recognition.

  • 96.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Gilani, Syed
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Road sign detection and recognition using fuzzy artmap: a case study Swedish speed-limit signs2006In: The 10th IASTED International Conference on Artificial Intelligence and Soft Computing, Palma de Mallorca, Spain, 2006Conference paper (Refereed)
    Abstract [en]

    In this paper, a novel approach is developed using Fuzzy ARTMAP Neural Networks to recognize and classify Swedish road and traffic signs. The Swedish Speed-Limit signs are selected as a case study, but the system can be applied to other signs. A new color detection and segmentation algorithm is presented in which the effects of shadows and highlights are eliminated. Images are taken by a digital camera mounted in a car. Segmented images are created by converting RGB images into HSV color space and applying the shadow-highlight invariant method. The method is tested on hundreds of outdoor images under shadow and highlight conditions, and it shows high robustness; in 95% of cases of correct segmentation is achieved. Classification is carried out by two stages of Fuzzy ARTMAP which are trained by 210 and 150 images, respectively. The first stage determines the border of the sign and the second stage determines the pictogram. Training and testing of both stages are made offline, using still images. In online mode, the system loads the Fuzzy ARTMAP and performs recognition process. An accuracy of 96.7% is achieved in Speed-Limit recognition and more than 90% as whole accuracy.

  • 97.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Jomaa, Diala
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Segmentation of low quality fingerprint images2010Conference paper (Refereed)
    Abstract [en]

    This paper presents a new algorithm to segment fingerprint images. The algorithm uses four features, the global mean, the local mean, variance and coherence of the image to achieve the fingerprint segmentation. Based on these features, a rule based system is built to segment the image. The proposed algorithm is implemented in three stages; pre-processing, segmentation, and post-processing. Gaussian filter and histogram equalization are applied in the pre-processing stage. Segmentation is applied using the local features. Finally, fill the gaps algorithm and a modified version of Otsu thresholding are invoked in the post-processing stage. In order to evaluate the performance of this method, experiments are performed on FVC2000 DB1. Segmentation of 100 images is performed and compared with manual examinations of human experts. It shows that the proposed algorithm achieves a correct segmentation of 82% of images under test.

  • 98.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Jomaa, Diala
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Davami, Erfan
    Segmentation of fingerprint images based on bi-level combination of global and local processing2012In: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 21, no 2, p. 97-120Article in journal (Refereed)
    Abstract [en]

    This paper presents a new approach to segment low quality fingerprint imageswhich are collected by low quality fingerprint readers. Images collected using such readersare easy to collect but difficult to segment. The proposed approach is based on combiningglobal and local processing to achieve segmentation of fingerprint images. On the globallevel, the fingerprint is located and extracted from the rest of the image by using a globalthresholding followed by dilation and edge detection of the largest object in the image.On the local level, fingerprint’s foreground and its border image are treated using differentfuzzy rules. These rules are based on the mean and variance of the block under consideration.The approach is implemented in three stages: pre-processing, segmentation, andpost-processing.Segmentation of 100 images was performed and compared with manual examinationsby human experts. The experiments showed that 96% of images under test are correctlysegmented. The results from the quality of segmentation test revealed that the averageerror in block segmentation was 2.84% and the false positive and false negatives wereapproximately 1.4%. This indicates the high robustness of the proposed approach.

  • 99.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Khan, Taha
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Pattern matching approach towards real-time traffic sign recognition2010Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of traffic sign recognition in real-time conditions. The algorithm presented in this paper is based on detecting traffic signs in life images and videos using pattern matching of the unknown sign’s shape with standard shapes of the traffic signs. The pattern matching algorithm works with shape vertices rather than the whole image. This reduces the computation time which is a crucial factor to fit real-time demands. The algorithm is translation and scaling invariant. It shows high robustness as it is tested with 500 images and several videos and a recognition rate of 97% is achieved.

  • 100.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Mohammed, Iman
    Night time vehicle detection2012In: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 21, no 2, p. 143-165Article in journal (Refereed)
    Abstract [en]

    Night driving is one of the major factors which affects traffic safety. Althoughdetecting oncoming vehicles at night time is a challenging task, it may improve trafficsafety. If the oncoming vehicle is recognised in good time, this will motivate drivers tokeep their eyes on the road. The purpose of this paper is to present an approach to detectvehicles at night based on the employment of a single onboard camera. This system isbased on detecting vehicle headlights by recognising their shapes via an SVM classifierwhich was trained for this purpose. A pairing algorithm was designed to pair vehicleheadlights to ensure that the two headlights belong to the same vehicle. A multi-objecttracking algorithm was invoked to track the vehicle throughout the time the vehicle isin the scene. The system was trained with 503 single objects and tested using 144 587single objects which were extracted from 1410 frames collected from 15 videos and 27moving vehicles. It was found that the accuracy of recognition was 97.9% and the vehiclerecognition rate was 96.3% which indicates clearly the high robustness attained by thissystem.

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