Visual tracking algorithm based on multi-attention and dual-template update

To address the problem of insufficient representational capability and lack of online update of the fully-convolutional Siamese network (SiamFC) tracker in complex scenes, this paper proposed a visual tracking algorithm based on multi-attention and dual-template update. First, the feature extraction network was constructed by replacing AlexNet with the VGG16 network, and SoftPool was used to replace the maximum pooling layer. Secondly, the multi-attention module (MAM) was added after the backbone network to enhance the network’s ability to extract object features. Finally, a dual template was designed for feature fusion and response map fusion, and average peak-to-correlation energy (APCE) was used to determine whether to update the dynamic templates, which effectively improved the tracking robustness. The proposed algorithm was trained on the GOT-10k dataset and tested on the OTB2015, VOT2018, and UAV123 datasets. The experimental results show that, compared with the benchmark algorithm SiamFC, the tracking success rate of the proposed algorithm is increased by 0.085 and 0.037, and the accuracy is increased by 0.118 and 0.058 on the OTB2015 and UAV123 datasets, respectively. On the VOT2018 dataset, the tracking accuracy, robustness, and expected average overlap (EAO) are improved respectively by 0.030, 0.295 and 0.139. The proposed algorithm achieves high tracking accuracy in complex scenes, and the tracking speed reaches 33.9 frame/s, which meets the real-time tracking requirements.