A hierarchical clustering of features approach for vehicle tracking in traffic environments

查看全文 作　　者：Anan [1]Banharnsakun;Supannee [2]Tanathong

机构地区：[1]CIR Laboratory,Faculty of Engineering at Sriracha,Kasetsart University Sriracha Campus,Chonburi,Thailand;[2]Laboratory for Sensor and Modeling,Department of Geoinformatics,University of Seoul,Seoul,Korea

出　　处：《International Journal of Intelligent Computing and Cybernetics》索引2016年第9卷第4期,共15页

摘　　要：Purpose-Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking.Especially in a traffic video monitoring system,vehicle detection is an essential and challenging task.In the previous studies,many vehicle detection methods have been presented.These proposed approaches mostly used either motion information or characteristic information to detect vehicles.Although these methods are effective in detecting vehicles,their detection accuracy still needs to be improved.Moreover,the headlights and windshields,which are used as the vehicle features for detection in these methods,are easily obscured in some traffic conditions.The paper aims to discuss these issues.Design/methodology/approach-First,each frame will be captured from a video sequence and then the background subtraction is performed by using the Mixture-of-Gaussians background model.Next,the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest in each foreground scene and the hierarchical clustering approach is then applied to cluster and form them into feature blocks.These feature blocks will be used to track the moving objects frame by frame.Findings-Using the proposed method,it is possible to detect the vehicles in both day-time and night-time scenarios with a 95 percent accuracy rate and can cope with irrelevant movement(waving trees),which has to be deemed as background.In addition,the proposed method is able to deal with different vehicle shapes such as cars,vans,and motorcycles.Originality/value-This paper presents a hierarchical clustering of features approach for multiple vehicles tracking in traffic environments to improve the capability of detection and tracking in case that the vehicle features are obscured in some traffic conditions. 关键词：Feature extraction Hierarchical clustering Mixture-of-Gaussians Multiple object detection Shi-Tomasi corner detection Vehicle tracking Background model

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A hierarchical clustering of features approach for vehicle tracking in traffic environments

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