Real-time hand detection and tracking using LBP features

Authors:
Bin Xiao;Xiang-min Xu;Qian-pei Mai
Affiliations:
School of Electronic and Information Engineering, South China University of Technology, Guangdong, Guangzhou, China;School of Electronic and Information Engineering, South China University of Technology, Guangdong, Guangzhou, China;School of Electronic and Information Engineering, South China University of Technology, Guangdong, Guangzhou, China
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Year:
2010

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Abstract

In this paper a robust and real-time method for hand detection and tracking is proposed. The method is based on AdaBoost learning algorithm and local binary pattern (LBP) features. The hand is detected by the cascade of classifiers with LBP features. A detailed study was developed to select the parameters for the hand detection classifiers. When tracking the hand, a region of interest (ROI) is defined based on the hand region detected in the last frame, and in order to improve robustness on rotation affine transformation is applied to the ROI. The experimental result demonstrates that this method can successfully detect the hand and track it in real-time.