Real-Time Hand Detection and Tracking Using LBP Features

摘要：

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 realtime.

关键词： hand detection hand tracking boosting LBP HCI

作者: Bin Xiao Xiang-min Xu Qian-pei Mai

作者单位: School of Electronic and Information Engineering, South China University of Technology Guangdong, Guangzhou 510640 China

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 282-289

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）