Journal
IEEE ACCESS
Volume 7, Issue -, Pages 81441-81450Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2922416
Keywords
Visual tracking; correlation filter; convolutional neural network; spatial constraint; temporal regularization
Categories
Funding
- National Natural Science Foundation of China [61571458]
Ask authors/readers for more resources
Correlation filters have achieved appealing performance with high speed in recent years. The advantage of correlation filter-based tracking methods is mainly attributed to powerful features and effective online filter learning. However, the periodic assumption of the training data would introduce unwanted boundary effects, which severely degrade the discrimination power of the correlation filter. In this paper, we construct the spatial reliable map with deep features from Convolutional Neural Network, then the map is used to adjust the filter support to the part of the object suitable for tracking. In order to further improve the long-term tracking ability, we introduce temporal regularization to DCF training, which can deal with occlusion and deformation situations. The experimental results show that the proposed algorithm achieves high tracking success rate and accuracy.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available