6 July 2015 Robust hand tracking with on-line and off-line learning
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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.
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Jiangyue Wei, Jiangyue Wei, Yong Zhao, Yong Zhao, Hao Liang, Hao Liang, Ruzhong Cheng, Ruzhong Cheng, Yiqun Wei, 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); doi: 10.1117/12.2197034; https://doi.org/10.1117/12.2197034
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