ROI-HOG and LBP based human detection via shape part-templates matching

Authors:
Shenghui Zhou;Qing Liu;Jianming Guo;Yuanyuan Jiang
Affiliations:
School of Automation, Wuhan University of Technology, Wuhan, China;School of Automation, Wuhan University of Technology, Wuhan, China;School of Automation, Wuhan University of Technology, Wuhan, China;School of Automation, Wuhan University of Technology, Wuhan, China
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Year:
2012

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Abstract

Currently, Histogram of Oriented Gradient (HOG) descriptor serves as the predominant method when it comes to human detection. To further improving its detection accuracy and decrease its large dimensions of feature vectors, we introduce an improved method in which HOG is extracted in the Region of Interest (ROI) of human body with a combined Local Binary Pattern (LBP) feature. Via establishing human shape part-templates tree, a template matching approach is employed to improve detection results and segment human edges. The experimental results on INRIA database and images from practical campus video surveillance demonstrate the effectiveness of our method.