Real-time object tracking based on improved fully-convolutional siamese network. (September 2020)

• Record Type: Journal Article
• Authors: Xu, Haisheng; Zhu, Youchan
• Is Part Of: Computers & electrical engineering. Volume 86(2020)
• Publication Date: 2020-09
• Subjects: Siamese network -- triplet loss -- multi-channel attention mechanism -- distractor-aware; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106755
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In recent years, the tracking model based on the Siamese Network has been widely used in the object tracking field to model the object tracking task as a similarity matching problem, which balances the tracking speed and accuracy. However, there are insufficient robustness, discriminative ability and generalization ability for object deformation and complex background interference. In this paper, an improved Fully-convolutional Siamese Network is proposed. The Triplet Loss function is used as the model objective function instead of logistic loss, and the multi-channel attention mechanism is introduced to make the model pay more attention to the tracking related information and enhance the model discriminating ability. In the offline training phase, an effective data augmentation strategy is used to control the uneven distribution of sample categories and improve the generalization ability of the model. In the tracking phase, the Distractor-aware module is used to transfer the general feature representation domain to a specific object domain, thereby improving model discriminating ability. In experiments, the results on VOT2016 tracking benchmark shows that our model has a significant improvement over the SiamFC tracker in multiple evaluation indicators.