Finger-Fist Detection in First-Person View based on Monocular Vision using Haar-Like Features

摘要：

　　This paper introduces a new idea for interaction between human and wearable device which is using finger-fist posture to be the detecting and tracking target.We built the detector with cascade classier using Haar-like features and the AdaBoost learning algorithm.The detector for the posture shows good tolerance for out-of-plane rotation and robustness against lighting variance and cluster background.With excellent real-time performance and high recognition accuracy,the detection can be acted as a tracker to track the path of fist in the first-person view.

关键词： Haar-like features AdaBoosting finger-fist first-person view HCI

作者: Wang Jingtao YU Chunxuan

作者单位: Beijing University of Technology,Beijing 100124,China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 4920-4923

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）