Multi -target Tracker Using Combined Motion and Appearance Model for Radar Video Data

Multi-target tracking is a difficult task in radar data processing field. To track multiple targets with the video data collected by a X-band surface surveillance radar, a two -level cascade detection algorithm is designed at first. We combine the motion model and appearance model of the targets together for multi-target tracking and propose a multi-target tracking algorithm based on scale adaptive kernelized correlation filter (SAKCF) and Kalman filter (KF) in this paper. Firstly, the extended targets are detected by the designed tow-level cascade detection algorithm and the centroids of the extended targets are extracted by centroid algorithm too. Then, a SAKCF tracker is assigned to each target. KF is utilized to predict the position of each target and each SAKCF tracker searches the region near the predicted position to determine the real position and scale of each target. The association between tracks and trace points is performed based on Hungary algorithm. Finally, the abnormal tracks are deleted. Experimental results based on the radar video data show that compared with the classical simple online real-time target tracking (SORT) algorithm and joint probabilistic data association (JPDA) algorithm, the proposed algorithm improves multi-object tracking precision (MOTP) and multi-object tracking accuracy (MOTA).