Multiple vehicle detection and segmentation

Abstract

Vision based system are widely used in the field of Intelligent Transportation System (ITS) to extract a large amount of information to analyze traffic scenes. Previously, this burdensome task was performed by human operator in traffic monitoring centre. Nevertheless, the increasing number of vehicles on the road as well as significant increase on cameras dictated the need for traffic surveillance systems. The research undertaken in this thesis is mainly concentrated on developing a multiple vehicle detection and segmentation focusing on monitoring through Closed Circuit Television (CCTV) video. The proposed system is able to automatically segment vehicle extracted from heavy traffic scene. In this work, optical flow estimation alongside with blob analysis technique is proposed in order to detect the moving vehicle. Since there is no reference background on the image, optical flow technique is used to distinguish between background from video scene with moving vehicle. Prior to segmentation, blob analysis technique will compute the area of interest region corresponding to moving vehicle which will be used to create bounding box on that particular vehicle. Experimental validation on the proposed system was performed and the algorithm is demonstrated on various set of traffic scene.