Nanchang Institute of Technology

Jun Wang

Peng Yin

Corresponding template features and search features are obtained by convolution operation, and respectively used as input features in aggregation modules. Intermediate features are obtained by performing convolution operation on the input features. The aggregation modules share the same convolution operation, and hybrid convolution in the aggregation module uses a depthwise convolution and a pointwise convolution to separate mixture between space and channel of the intermediate features. Redundancy in spatial and channel features is reduced while increasing receptive field. Self-attention module in the aggregation module learns intermediate features, and adaptively focuses on different regions to capture more global correlations. Output features of the hybrid convolution are added to output features of the self-attention module to pass through a drop-out layer to obtain final output features. The output features aggregate local and global context information. Overfitting of network is alleviated during training, thereby improving generalization ability of tracker.

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Nanchang, China

Jun Wang