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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
会议名称Pacific-Rim Conference on Multimedia
会议录名称PCM 2012
会议日期4-6 December 2012
会议地点Singapore, Singapore
摘要We 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.
关键词Active Transfer Learning Object Classification Visual Surveillance
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/13246
专题类脑智能研究中心
通讯作者Zhaoxiang Zhang
推荐引用方式
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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