Correlation Filter Tracking Based on Deep Spatial Regularization
PU Lei1, FENG Xin-xi2, HOU Zhi-qiang3, ZHA Yu-fei4, YU Wang-sheng2
1. Graduate College, Air Force Engineering University, Xi'an, Shaanxi 710077, China;
2. Institute of Information and Navigation, Air Force Engineering University, Xi'an, Shaanxi 710077, China;
3. School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China;
4. School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
Abstract：In recent years,the correlation filter based algorithm combined with deep features has received extensive attention.The period assumption of the training samples improves the computational efficiency,but also introduces the boundary effect,which limits the further improvement of the tracking performance.By exploring the deep feature representation ability,a new tracking framework is proposed.Since the deep features have good semantic information,the fifth layer convolution feature of VGG network is used to extract the spatially reliable region of the target,and the region information is introduced into the objective function to establish a spatial constraint model.Then iteratively solved by ADMM algorithm.In order to further improve the long-time tracking ability,a simple and effective method of occlusion detection is proposed.Experimental results show that the proposed algorithm outperforms most advanced algorithms in tracking accuracy and success rate.
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2020, Vol. 48 
