Object tracking based on siamese network with 3D attention and multiple graph attention

Abstract

Currently, the object tracking algorithm based on the siamese network is the most popular research direction of object tracking. However, most siamese network trackers are unable to update the template, resulting in hardly dealing with the fuzzy target well, which requires the trackers to extract the target features in the template more accurately. Moreover, most of the siamese network trackers compute the similarity between the target template and the search region in a global matching way. Thus they cannot well handle the rapid change of the target. To address the above limitations, we propose an object tracking algorithm based on 3D attention SimAM and multiple graph attention. We combine siamese networks with 3D attention to improve the feature extraction capability of the network. We frame the target template and search region to improve feature-matching accuracy by introducing multiple graph attention mechanisms. We test our model against three authoritative benchmarks, GOT10K, UAV123 and OTB100, and compare it with advanced trackers, and the results show that our trackers achieve better results.