Target tracking algorithm combining deformable convolution and global information
A target tracking algorithm based on regional candidate twin networks(SiamRPN),and combining deformable convolution and global information.Firstly,backbone network with moderate computation cost is used to improve the feature extraction ability of the model.Secondly,the global context attention module is used to improve the ability of global information modeling.In the part of similarity measurement,the deformable cross-correlation module is designed to aggregate template features and search features.Finally,the multi-layer feature fusion strategy is adopted to thoroughly mine the deep semantic information and shallow positioning information,so that to make target localization and classification more accurate.Experimental results show that this algorithm is obviously better than mainstream trackers in the comparision.In two datasets of target tracking OTB 100 and VOT 2016,the success rate and EAO are improved by 5.3%and 8.5%respectively,and the tracking speed reaches 68 fps,realize ultra real-time tracking,which proves the validity of the proposed algorithm.
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