Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6941578 | Signal Processing: Image Communication | 2018 | 32 Pages |
Abstract
Correlation filter and Siamese network based methods are popular in visual tracking due to their competitive performance in both precision and efficiency. However, Siamese network based trackers tend to drift towards similar target regions due to the semantic feature representations and an absence of model update. To overcome this limitation, in this paper, we incorporate correlation filter trackers with the fully-convolutional Siamese network (SiamFC) to achieve distractor-aware tracking. First, the pretrained network in SiamFC is utilized to obtain the response map in a large search area; then a guide line is designed to screen out the potential candidate positions of the target according to the distribution of the computed response map; finally, correlation filter tracker based on HOG features is employed to identify the target location from these candidates. Our method combines the advantage of end-to-end tracking based on deep features in SiamFC and online model-update in correlation filter trackers based on hand-crafted features. Experimental results on OTB and VOT benchmarks show that our proposed algorithm performs favorably in terms of both accuracy and robustness when compared to state-of-the-art trackers while maintaining real-time tracking speed.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Yangliu Kuai, Gongjian Wen, Dongdong Li,