Dual-Template Siamese Network with Attention Feature Fusion for Object Tracking

Abstract

In order to alleviate the adverse effects resulted from complex scenes for object tracking, such as fast movement, mottled background, interference of similar objects, and occlusion etc., an algorithm using dual-template Siamese network with attention feature fusion, named SiamDT, is proposed in this paper. The main idea include that the original ResNet-50 network is improved to extract deep semantic information and shallow spatial information, which are effectively fused using the attention mechanism to achieve accurate feature representation of objects. In addition, a template branch is added to the traditional Siamese network in which a dynamic template is generated together with the first frame image to solve the problems of template failure and model drift. Experimental results on OTB100 dataset and VOT2018 dataset show that the proposed approach obtains the excellent performance compared with the state-of-the-art tracking algorithms, which verifies the feasibility and effectiveness of the proposed approach.

Keywords

• object tracking
• siamese network
• feature extraction
• feature fusion
• attention mechanism