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Transferring Training Instances for Convenient Cross-View Object Classification in Surveillance
Zhaoxiang Zhang; Yuhang Zhao; Yunhong Wang; Jianyun Liu; Zhenjun Yao; Jun Tang
Source PublicationIEEE Transactions on Information Forensics and Security
2013-05-29
Volume8Issue:10Pages:1632-1641
AbstractAutomatic object classification is an important issue in traffic scene surveillance. Appearance variation due to perspective distortion is one of the most difficult problems for moving object detection, tracking, and recognition. We propose an active transfer learning approach to bridge the gap between appearance variations under two different scenes. Only a small number of training samples are required in the target scene, which can be combined with transferred samples of the source scene to achieve a reliable object classifier in the target scene, and active learning strategy makes the algorithm more efficient. Abundant experiments are conducted and experimental results demonstrate the effectiveness and convenience of our approach.
KeywordTransfer Learning Active Learning Cross-view Object Classification Surveillance
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13202
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Zhaoxiang Zhang,Yuhang Zhao,Yunhong Wang,et al. Transferring Training Instances for Convenient Cross-View Object Classification in Surveillance[J]. IEEE Transactions on Information Forensics and Security,2013,8(10):1632-1641.
APA Zhaoxiang Zhang,Yuhang Zhao,Yunhong Wang,Jianyun Liu,Zhenjun Yao,&Jun Tang.(2013).Transferring Training Instances for Convenient Cross-View Object Classification in Surveillance.IEEE Transactions on Information Forensics and Security,8(10),1632-1641.
MLA Zhaoxiang Zhang,et al."Transferring Training Instances for Convenient Cross-View Object Classification in Surveillance".IEEE Transactions on Information Forensics and Security 8.10(2013):1632-1641.
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