Abstract
Recently, the Siamese network based visual tracking methods have shown great potentials in balancing the tracking accuracy and computational efficiency. These methods use two-branch convolutional neural networks (CNNs) to generate a response map between the target exemplar and each of candidate patches in the search region. However, since these methods have not fully exploit the target-specific information contained in the CNN features during the computation of the response map, they are less effective to cope with target appearance variations and background clutters. In this paper, we propose a Target-Specific Response Attention (TSRA) module to enhance the discriminability of these methods. In TSRA, a channel-wise cross-correlation operation is used to produce a multi-channel response map, where different channels correspond to different semantic information. Then, TSRA uses an attention network to dynamically re-weight the multi-channel response map at every frame. Moreover, we introduce a shortcut connection strategy to generate a residual multi-channel response map for more discriminative tracking. Finally, we integrate the proposed TSRA into the classical Siamese based tracker (i.e., SiamFC) to propose a new tracker (called TSRA-Siam). Experimental results on three popular benchmark datasets show that the proposed TSRA-Siam outperforms the baseline tracker (i.e., SiamFC) by a large margin and obtains competitive performance compared with several state-of-the-art trackers.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.