| Citation: | BAI Luo, ZHANG Hongli, WANG Conget al. Target tracking algorithm based on efficient attention and context awareness[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(7): 1222-1232. doi: 10.13700/j.bh.1001-5965.2021.0013(in Chinese)
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The matching-based Siamese network algorithm often lacks the overall perception of a target, which easily leads to inaccurate target state estimation and target missing in complex environments. Therefore, this paper designs two lightweight modules on the basis of the twin network to achieve more accurate and robust target tracking. An efficient channel attention module is embedded into the backbone network after its construction for feature extraction. Efficient extraction of target features and enhanced differential representation are achieved. so that the network pays more attention to the target information. The features after template matching pass a local context awareness module, thus enhancing the network's overall perception of the target to deal with the complex and changeable environment in the tracking process. The Anchor-free state estimation strategy is used to achieve accurate estimation of the target. Experimental results show that on the datasets OTB100, VOT2016 and VOT2018, SiamCC algorithm outperforms DaSiamRPN algorithms and ATOM algorithm, with the tracking speed reaching 85 frame/s.
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