Research on Siamese network algorithm based on parallel channel attention mechanism for target tracking

Abstract

In order to effectively improve the tracking performance of the target in various complex environment in the tracking process, the reinforcement research of target features has become one of the important work. In this paper, a Siamese network target tracking algorithm based on parallel channel attention mechanism (PCAM) is proposed by combining feature cascade algorithm with visual attention. Firstly, the characteristics of SENet network and ECA network are fully analyzed. Secondly, the parallel channel attention mechanism is constructed based on ECA module, which integrates global average pooling and maximum pooling. Parallel channel attention mechanism not only solves the problem of channel correlation reduction of SENet module, but also solves the problem of target feature information enhancement. Thirdly, the output model of channel attention is used as the input of spatial attention model to realize the effective complement to channel attention mechanism. By calculating the weight value of different spatial locations, the structural relation between spatial location information is constructed, the feature expression ability of the model is enhanced. Finally, the algorithm is evaluated on standard data sets OTB100, OTB2013, OTB2015, VOT2016 and VOT2018. Experimental results show that the PCAM has stronger feature extraction performance for complex environment, higher target tracking accuracy and robustness, and has strong advantages compared with other comparative experiments.