Learning Temporally Correlated Representations Using Lstms for Visual Tracking

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	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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