Target Re-detection and Tracking Based on Correlated Filter
Yuqiu Wu, Zhaohui Li, Dongmei Li · 2018
Visual object tracking is a key research field in computer vision. Correlation filter tracker (CFT) based methods have shown the strong ability to track objects. However, they did not consider re-detecting the target when tracking failed, which leads to the bad performance. In order to solve this challenge, we propose a novel tracking algorithm that combines the correlation filter tracker and particle filter tracker to re-detect the target. Our algorithm consists three steps: First, we use the correlation filter tracker to track the target and calculate the average peak correlation energy (APCE). Then, we compare the APCE with the default threshold value to determine whether the tracking target misses. When the target missed, particle filter algorithm is used to re-detect the target and the location of the target is obtained in the current frame based on the location of the target in the previous frame. Compared with the staple tracking algorithm, experimental results show that the proposed algorithm is superior to the staple algorithm in distance precision and success rate. Besides, out algorithm operates fast and can meet the real-time requirement.