1. School of Computer Science and Engineering, Shenyang Jianzhu University, Shenyang 110168; 2. Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction, Shenyang Jianzhu University, Shenyang 110168; 3. Shenyang Branch of National Special Computer Engineering Technology Research Center, Shenyang Jianzhu University, Shenyang 110168; 4. School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168
Abstract：The high computational complexity of current Siamese network based target tracking algorithm during the candidate box generation stage results in poor real-time performance and reduced accuracy in complex scenarios. To address these issues, an anchor-free RepPoints and attention mechanism based adaptive Siamese network for object tracking is proposed. First, a large-kernel convolutional attention module is introduced in the backbone network of the Siamese subnetwork to extract global features of the target, enhancing the precision and generalization ability of the model. Second, a module for anchor-free multi-RepPoints is utilized to learn multiple RepPoints of the target, and then an adaptive learning weight coefficient module is employed to filter out more accurate target RepPoints, further improving model precision and robustness. Finally, RepPoints are transformed into predicted boxes, thereby eliminating the need for predefined candidate boxes, reducing computational complexity and enhancing real-time tracking performance. Experiments indicate that the proposed method achieves significant improvements in precision and success rate on four datasets.
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2024, Vol. 37 
