Knowledge Commons of Institute of Automation,CAS
Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking | |
Wang, Qiang1,3; Teng, Zhu2; Xing, Junliang3; Gao, Jin3; Hu, Weiming3; Stephen Maybank4 | |
2018-06 | |
会议名称 | IEEE Conference on Computer Vision and Pattern Recognition |
会议日期 | 2018-7 |
会议地点 | Salt Lake City, Utah, USA |
摘要 | Offline training for object tracking has recently shown great potentials in balancing tracking accuracy and speed. However, it is still difficult to adapt an offline trained model to a target tracked online. This work presents a Residual Attentional Siamese Network (RASNet) for high performance object tracking. The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt the model without updating the model online. In particular, by exploiting the offline trained general attention, the target adapted residual attention, and the channel favored feature attention, the RASNet not only mitigates the over-fitting problem in deep network training, but also enhances its discriminative capacity and adaptability due to the separation of representation learning and discriminator learning. The proposed deep architecture is trained from end to end and takes full advantage of the rich spatial temporal information to achieve robust visual tracking. Experimental results on two latest benchmarks, OTB-2015 and VOT2017, show that the RASNet tracker has the state-of-the-art tracking accuracy while runs at more than 80 frames per second.
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收录类别 | EI |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39070 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 中国科学院自动化研究所 |
作者单位 | 1.University of Chinese Academy of Sciences, Beijing, China 2.School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China 3.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 4.Department of Computer Science and Information Systems, Birkbeck College, University of London, UK |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Wang, Qiang,Teng, Zhu,Xing, Junliang,et al. Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking[C],2018. |
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王强_CVPR2018.pdf(2612KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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