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Sensors (Basel). 2019 Apr 18;19(8). pii: E1858. doi: 10.3390/s19081858.

Online Siamese Network for Visual Object Tracking.

Author information

1
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China. changshuo@bupt.edu.cn.
2
Department of Electrical Engineering, Northern Illinois University, Dekalb, IL 60115, USA. weili@ieee.org.
3
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China. zhangyf@bupt.edu.cn.
4
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China. fengzy@bupt.edu.cn.

Abstract

Offline-trained Siamese networks are not robust to the environmental complication in visual object tracking. Without online learning, the Siamese network cannot learn from instance domain knowledge and adapt to appearance changes of targets. In this paper, a new lightweight Siamese network is proposed for feature extraction. To cope with the dynamics of targets and backgrounds, the weight in the proposed Siamese network is updated in an online manner during the tracking process. In order to enhance the discrimination capability, the cross-entropy loss is integrated into the contrastive loss. Inspired by the face verification algorithm DeepID2, the Bayesian verification model is applied for candidate selection. In general, visual object tracking can benefit from face verification algorithms. Numerical results suggest that the newly developed algorithm achieves comparable performance in public benchmarks.

KEYWORDS:

Bayesian verification; Siamese network; improved contrastive loss; visual object tracking

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