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Proceedings Paper

A novel online learning method for head detection in video sequences
Author(s): Dapeng Luo; Nong Sang; Rui Huang; Xiaojun Tong
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Paper Abstract

Online learning is an effective incremental learning method. Compared with the conventional off-line learning method, online learning updates the original classifier continuously with new samples and improves its performance. In this paper, we propose a novel online learning framework for head detection in video sequences. At first, an off-line classifier is trained with a few labeled samples. And it was used to object detection in video sequences. Based on online boosting algorithm, the detected objects will be used to train the classifier as new samples. Instead of using another detection algorithm to label the new sample automatically like other online learning framework, we ensure the correct label from tracking. Furthermore, the weights of new samples can be obtained from tracking directly. Thus the training speed of the classifier can be improved. Experimental results on two video datasets are provided to show the efficient and high detection rate of the framework.

Paper Details

Date Published: 15 April 2010
PDF: 8 pages
Proc. SPIE 7701, Visual Information Processing XIX, 77010Q (15 April 2010); doi: 10.1117/12.849501
Show Author Affiliations
Dapeng Luo, Huazhong Univ. of Science and Technology (China)
Nong Sang, Huazhong Univ. of Science and Technology (China)
Wuhan Polytechnic Univ. (China)
Rui Huang, Huazhong Univ. of Science and Technology (China)
Xiaojun Tong, Wuhan Polytechnic Univ. (China)


Published in SPIE Proceedings Vol. 7701:
Visual Information Processing XIX
Zia-ur Rahman; Stephen E. Reichenbach; Mark A. Neifeld, Editor(s)

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