Human Detection Method Based on Multi-Part Detector and Multi-Instance Learning
DING Jian-Hao1,2, GENG Wei-Dong1, WANG Yi-Gang2
1. State Key Laboratory of CAD CG,Zhejiang University,Hangzhou 310027 2.Institute of Computer Graphics and Image Processing,Hangzhou Dianzi University,Hangzhou 310018
Abstract：Part-based detection methods can deal with large articulated pose variations of human target and partial occlusions. Multi-instance learning is employed in content-based image retrieval and scene understanding, because it is good at handling the inherent ambiguity of images.A human detection method based on multi-part and multi-instance learning methods is presented. Firstly, the training samples are partitioned into several regions containing multi-instance according to body structure. Then, the part detectors are trained by using multiple instance learning method based on AdaBoost algorithm. After that the responding scores from the training samples tests are obtained by using the individual part detector when predicting on the positive and negative training bags. Therefore, the training samples are converted to feature vectors composed of part scores. The final assemble detector is learned using a linear SVM method. The experimental results on INRIA database show that the proposed approach improves the detection performance in single instance learning and the influence of the three different multi-part divisions on detection performance is evaluated.
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2012, Vol. 25 
