SiamCAM: A Real-Time Siamese Network for Object Tracking with Compensating Attention Mechanism

Abstract

The Siamese-based object tracking algorithm regards tracking as a similarity matching problem. It determines the object location according to the response value of the object template to the search template. When there is similar object interference in complex scenes, it is easy to cause tracking drift. We propose a real-time Siamese network object tracking algorithm combined with a compensating attention mechanism to solve this problem. Firstly, the attention mechanism is introduced in the feature extraction module of the template branch and search branch of the Siamese network to improve the feature representation of the network to the object. The attention mechanism of the search branch enhances the feature representation of both the target and the similar backgrounds simultaneously. Therefore, based on the above two-branch attention, we propose a compensated attention model, which introduces the attention selected by the template branch into the search branch, and improves the discriminative ability of the search branch to the object by using the feature attention weighting of the template branch to the object. Experimental results on three popular benchmarks, including OTB2015, VOT2018, and LaSOT, show that the accuracy and robustness of the algorithm in this paper are adequate. It improved occlusion cases, similar object interference, and high-speed motion. The processing speed on GPU reaches 47 fps, which can achieve real-time object tracking.