关键词: 压缩跟踪算法, 实时跟踪, 目标检测, 卡尔曼滤波

Abstract: We employ compressive tracking (CT) algorithm to detect and track motion cars based on the characteristics of traffic monitoring video. The algorithm has strong robustness for greater camera change, motion vehicles scale change, and background change. However, the algorithm is easy to fail when a vehicle is sheltered. We therefore present a Kalman filter based modified CT algorithm to predict the motion trail of a sheltered vehicle. Kalman filter can track the trail of a target with CT algorithm, and effectively predict the trail of a sheltered target. Experimental results show that the algorithm can not only better solve the issue of motion vehicles scale change-precisely and stably track a vehicle when it disappears or is partly sheltered-,but also has better realtime performance. It thus satisfies the requirements of engineering application.

Key words: compressive tracking algorithm, realtime tracking, target detection, Kalman filter