Fusing Kalman filter with TLD algorithm for target tracking
Chengjian Sun, Songhao Zhu, Jiawei Liu · 2015
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.