Robust hand tracking with on-line and off-line learning

Jiangyue Wei; Yong Zhao; Hao Liang; Ruzhong Cheng; Yiqun Wei

doi:10.1117/12.2197034

6 July 2015 Robust hand tracking with on-line and off-line learning

Jiangyue Wei, Yong Zhao, Hao Liang, Ruzhong Cheng, Yiqun Wei

Proceedings Volume 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015); 96312G (2015) https://doi.org/10.1117/12.2197034
Event: Seventh International Conference on Digital Image Processing (ICDIP15), 2015, Los Angeles, United States

Abstract

Hand tracking is becoming more and more popular in the field of human-computer interaction (HCI). A lot of studies in this area have made good progress. However, robust hand tracking is still difficult in long-term. On-line learning technology has great potential in terms of tracking for its strong adaptive learning ability. To address the problem we combined an on-line learning technology called on-line boosting with an off-line trained detector to track the hand. The contributions of this paper are: 1) we propose a learning method with an off-line model to solve the drift of on-line learning; 2) we build a framework for hand tracking based on the learning method. The experiments show that compared with other three methods, the proposed tracker is more robust in the strain case.

Citation Download Citation

Jiangyue Wei, Yong Zhao, Hao Liang, Ruzhong Cheng, and Yiqun Wei "Robust hand tracking with on-line and off-line learning", Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96312G (6 July 2015); https://doi.org/10.1117/12.2197034

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