Semi-supervised vehicle recognition: an approximate region constrained approach

Authors:
Rui Zhao;Zhihua Wei;Duoqian Miao;Yan Wu;Lin Mei
Affiliations:
Department of Computer Science and Technology, Tongji University, Shanghai, China,The Third Research Institute of the Ministry of Public Security, Shanghai, China;Department of Computer Science and Technology, Tongji University, Shanghai, China,State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;Department of Computer Science and Technology, Tongji University, Shanghai, China;Department of Computer Science and Technology, Tongji University, Shanghai, China;The Third Research Institute of the Ministry of Public Security, Shanghai, China
Venue:
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Year:
2012

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Abstract

Semi-supervised learning attracts much concern because it can improve classification performance by using unlabeled examples. A novel semi-supervised classification algorithm SsL-ARC is proposed for real-time vehicle recognition. It makes use of the prior information of object vehicle moving trajectory as constraints to bootstrap the classifier in each iteration. Approximate region interval of trajectory are defined as constraints. Experiments on real world traffic surveillance videos are performed and the results verify that the proposed algorithm has the comparable performance to the state-of-the-art algorithms.