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Active Learning for Transferrable Object Classification in Cross-View Traffic Scene Surveillance
Zhaoxiang Zhang; Jun Tang; Yuhang Zhao; Yunhong Wang
2012-12-04
Conference NamePacific-Rim Conference on Multimedia
Source PublicationPCM 2012
Conference Date4-6 December 2012
Conference PlaceSingapore, Singapore
AbstractWe discuss the problem of object classification in cross-view traffic scene surveillance videos in this paper. To classify moving objects in traffic scene videos into pedestrian, bicycle and variety of vehicles, an effective intelligent classification framework has been proposed which takes advantage of a transfer machine learning method to bridge the gap between source scene data and target scene data. The transfer learning algorithm makes one classifier adaptive to perspective changes instead of training two different classifiers for corresponding perspectives. The samples transferred from source scene database have saved much manual labeling work on target scene database. In this paper, we propose an active transfer learning method to decrease manual labeling work further for target scene traffic object classification. Redundant experiments are conducted and experimental results demonstrate the effectiveness and convenience of our approach.
KeywordActive Transfer Learning Object Classification Visual Surveillance
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13246
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Zhaoxiang Zhang,Jun Tang,Yuhang Zhao,et al. Active Learning for Transferrable Object Classification in Cross-View Traffic Scene Surveillance[C],2012.
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