A Robust Object Tracking Algorithm Based on Surf and Kalman Filter
Hongpeng Yin, Chao Peng, Yi Chai, Fan Qu · Intelligent Automation & Soft Computing · 2013
In this paper, an efficient robust object tracking approach based on SURF and Kalman Filter is proposed. SURF as an outstanding local invariant feature is employed. Based on the SURF feature, a SURF match method is proposed. A combination method using an ingenious method and KF is used to predict the possible region, in which the tracking object may appear. Only in this region, SURF features are extracted and matched. It can significantly reduce the computational complexity. A histogram-based re-match process is employed to dislodge failure tracking after SURF matching. To verify the performance of the proposed algorithm, several comparative experiments are conducted. The results reveal that the proposed method achieves better performance and accuracy than conventional methods.