Feature-based tracking approach for detection of moving vehicle in traffic videos

Abstract

In this paper, we present a novel approach for detection of moving vehicles in traffic videos. We propose a feature-based (corner-based) tracking to track and classify moving vehicles from the extracted ghost or cast shadow. The corner points of the vehicles are detected, labeled and grouped to generate a unique label per vehicle. This approach is able to deal with different types of deformations on the shape of the vehicles due to changes in size, direction and viewpoint. Also, the proposed method is totally free from motion estimation. To demonstrate the robustness and accuracy of our system, the results of the experiments are conducted on traffic videos including different complex background, illumination, motion, camera position, clutter and direction of the vehicles taken from outdoor boulevards and city roads. We detect moving vehicles on an average of 98.8% in a scene. The results show the robustness of our proposed algorithm.