LAUSR

Multi-object tracking involves maintaining several trajectories of different objects moving in the scene throughout the video. With this objective, in this article, a fully automatic and real-time tracking algorithm to track multiple vehicles in a video is proposed. The proposed method specifically tries to address the challenges of occlusion and fast-motion in traffic surveillance. The algorithm begins with automatic detection of the moving targets by an adaptive GMM-based background subtraction method. The trajectories of these detected targets are then built using a three-level multi-motion modeled particle filter framework which allows to deal with the challenges of occlusion and fast-motions of the target. The likelihood model for targets is based on their color distribution and edge oriented histogram features. It is contended that the color distribution feature, which can represent the target appearance and the edge oriented histogram, which can describe the target structure are sufficient to represent it in an unique feature space. Based on the similarity of target likelihood, the locations of the targets are filtered. These filtered locations are then associated with the most likely detections using the proposed low-cost and fast data association algorithm based on Euclidean distance and prevailing motion vector. The performance evaluation of the proposed scheme is carried out based on the six measures: Multi Object Tracking Accuracy, Multi Object Tracking Precision, Mostly Tracked trajectories, Mostly Lost trajectories, Identity Switches and Frames Per Second. The results evaluated on stationary camera shot sequences from benchmark datasets as well as real-time shot videos indicate that the proposed algorithm ensures robust tracking and can be used effectively for real-time surveillance of highways.