Learning Temporally Correlated Representations Using Lstms for Visual Tracking | |
Qiaozhe Li; Xin Zhao; Kaiqi Huang | |
2016 | |
会议名称 | International Conference on Image Processing |
会议录名称 | Image Processing (ICIP), 2016 IEEE International Conference on |
页码 | 2381-8549 |
会议日期 | 2016-09-01 |
会议地点 | Phoenix, USA |
摘要 | In this paper, we propose to learn object representations with inference from temporal correlation in videos to achieve effective visual tracking. Unlike traditional methods which perform feature learning either at image level or based on intuitive temporal constraint, we employ the recurrent network with Long Short Term Memory (LSTM) units to directly learn temporally correlated representations of the objects in long sequences. The recurrent network is pre-trained offline with auxiliary data and then online optimized to adapt to the target-specific object. A structured SVM is employed to account for the temporally correlated object appearance as well as distinguish the object from background distraction. Experiment results not only show that the appearance and dynamic patterns of the objects can be characterized via temporally correlated feature learning, but also demonstrate that the proposed tracking algorithm performs favorably against the state-of-the-art methods. |
关键词 | Long Short Term Memory Structured Svm Temporally Correlated Feature Learning visual Tracking |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/12676 |
专题 | 模式识别实验室 |
通讯作者 | Kaiqi Huang |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Qiaozhe Li,Xin Zhao,Kaiqi Huang. Learning Temporally Correlated Representations Using Lstms for Visual Tracking[C],2016:2381-8549. |
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