CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Guo, Wen1,2; You, Sisi1,2; Gao, Junyu3; Yang, Xiaoshan3; Zhang, Tianzhu3; Xu, Changsheng3
Source Publication中国科学
Other AbstractWhile traditional tracking-by-detection methods have some robustness in object tracking, their simple classification of the target and the background cannot model the relative structural relationship between the target and the background. It is the lack of relative structural discriminative information that always causes a tracker drifting away. In order to alleviate the tracking drifting problem, we propose a new visual travel approach based on deep relative metric learning. In this study, we design a deep relative metric learning model with a symmetric and shared-weight deep convolutional neural network. Through such a network, we can explore the relative structural relationship between the target and the background in large-scale image patches. Then, the highest score of relative metric is used to locate the tracking object in the Bayesian tracking framework. The whole tracking algorithm is simple and effective. Experimental results on the tracking benchmark show that the proposed algorithm achieves a better tracking precision rate and success rate than other state-of-the-art tracking methods.
KeywordVisual Tracking Convolutional Neural Network (Cnn) Relative Attribute Metric Learning
Document Type期刊论文
Affiliation1.School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai 264009, China
2.Key Laboratory of Sensing Technology and Control in Universities of Shandong, Yantai 264009, China
3.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Recommended Citation
GB/T 7714
Guo, Wen,You, Sisi,Gao, Junyu,等. 深度相对度量学习的视觉跟踪[J]. 中国科学,2018,48(1):60-78.
APA Guo, Wen,You, Sisi,Gao, Junyu,Yang, Xiaoshan,Zhang, Tianzhu,&Xu, Changsheng.(2018).深度相对度量学习的视觉跟踪.中国科学,48(1),60-78.
MLA Guo, Wen,et al."深度相对度量学习的视觉跟踪".中国科学 48.1(2018):60-78.
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