Fusing Kalman filter with TLD algorithm for target tracking

As one of the core content of intelligent monitoring, target detection and tracking is the basis for video content analysis and understanding. Tracking-Learning-Detection is considered as a highly efficient algorithm for tracking a single target. Although this algorithm can re-track a target when the target is occluded by other targets, there still exists many shortcomings. This paper deals with the issue of target tracking by fusing Kalman filter with tracking-learning-detection algorithm. Specifically, an improved Kalman filter is first utilized to enhance the reliability of tracking-learning-detection algorithm; then, the area of the target is estimated to reduce the detection region and to increase the processing speed. Experimental results conducted on PETS2009/2010 benchmark video library demonstrate that the proposed method can detect properly and track accurately an target in complex scenes.