Active Learning for Transferrable Object Classification in Cross-View Traffic Scene Surveillance

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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	智能感知与计算研究中心
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.