Anthony Peng

Anti-occlusion object tracking based on correlation filter

Signal, Image and Video Processing, 2019

Jun Liu
Gang Xiao
Ping Ye
Xingzhong Xiong

Abstract

Despite remarkable progress, visual object tracking is still a challenging task as objects usually suffer from significant appearance changes, fast motion, and serious occlusion. In this paper, we propose an anti-occlusion correlation filter-based tracking method (AO-CF) for robust visual tracking. We first propose an occlusion criterion based on continuous response values. Based on the criterion, objects are divided into four categories to adaptively identify the occlusion of objects. Then we propose a new detection condition for detecting proposals. When the occlusion criterion is triggered, the re-detection mechanism is executed and the tracker is commanded to stop, and then the re-detector selects the most reliable proposal to reinitialize the tracker. Experimental results show that our method outperforms other state-of-the-art trackers in terms of both precision rate and success rate on the widely used object tracking benchmark dataset. In addition, AO-CF is able to achieve real-time tracking speed.

BibTeX

			
@article{liu2019anti,
  title={Anti-occlusion object tracking based on correlation filter},
  author={Liu, Jun and Xiao, Gang and Zhang, Xingchen and Ye, Ping and Xiong, Xingzhong and Peng, Shengyun},
  journal={Signal, Image and Video Processing},
  year={2019},
  volume={14},
  pages={753-761},
  doi={10.1007/s11760-019-01601-6}
}