Abstract
Herein, a novel automatic hand-tracking approach based on temporal histogram features is proposed. Our method utilizes the joint temporal weighted histogram (JTWH) to track the hand robustly. When tracking begins, the hand model is initialized using a hand detector. During the tracking process, the hand model is updated using the most recent frame data and the hand tracker uses the weighted temporal model to track the hand persistently and robustly. The weights are calculated using the temporal and spatial similarity between the hand model and the current tracked hand. Because hand movement can be fast and may produce deformation, a weighted histogram was selected for the single hand model. Experiments demonstrate the proposed algorithm’s ability to track the moving hand robustly in comparison with several traditional hand-tracking algorithms. The proposed approach is robust in the complex background, and it can track the hand quickly and effectively.
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References
Chang C, Chen I, Huang Y. Hand pose recognition using curvature scale space pattern recognition. In: Proceedings of the 16th International Conference on Pattern Recognition; IEEE Conference Publications, New Jersey; 2002. vol. 2, p. 386–89.
Stenger B. Template-based hand pose recognition using multiple cues. In: Proceedings of the 7th Asian Conference on Computer Vision, ACCV2006; Springer, Berlin, Germany; 2006. p. 551–60.
Ong E, Bowden R. A boosted classifier tree for hand shape detection. In: Proceedings of the 6th International Conference on Automatic Face and Gesture Recognition; IEEE Conference Publications, New Jersey; 2004. p. 889–94.
Hadid A. The local binary pattern approach and its applications to face analysis. In: International Conference on Image Processing Theory, Tools and Applications; IEEE Conference Publications, New Jersey; 2008. p. 1–9.
Erol A, Bebis G, Nicolescu M, Boyle RD, Twombly X. Vision-based hand pose estimation: a review. In: Proceedings of the Computer Vision and Image Understanding; IEEE Conference Publications, New Jersey; 2007. p. 52–73.
Bazzani L, Cristani M, Murino V. Decentralized particle filter for joint individual-group tracking. In: Proceedings of the CVPR2012; IEEE Conference Publications, New Jersey; 2012. p. 1886–93.
Zhang D, Javed O, Shah M. Video object segmentation through spatially accurate and temporally dense extraction of primary object regions. In: Proceedings of the CVPR2013; IEEE Conference Publications, New Jersey; 2013. p. 628–35.
Angelova A, Zhu S. Efficient object detection and segmentation for fine-grained recognition. In: Proceedings of the CVPR2013; IEEE Conference Publications, New Jersey; 2013. p. 811–18.
Kalal Z, Mikolajczyk K, Matas J. Face-TLD: tracking-learning-detection applied to faces. In: International Conference on Image Processing; IEEE Conference Publications, New Jersey; 2010. p. 3789–92.
Chen Q, Georganas N, Petriu E. Real-time vision-based hand gesture recognition using haar-like features. In: IEEE Instrumentation and Measurement Technology Conference Proceedings; IEEE Conference Publications, New Jersey; 2007. p. 1–6.
Li Z, Guo J, Cheong L-F, Zhou SZ. Perspective motion segmentation via collaborative clustering. In: Proceedings of the ICCV2013; IEEE Conference Publications, New Jersey; 2013. p. 1369–76.
Zuo W, Zhang L, Song C, Zhang D. Texture-preserved image denoising via gradient histogram estimation and matching. In: Proceedings of the CVPR2013; IEEE Conference Publications, New Jersey; 2013. p. 1203–10.
Weinzaepfel P, Revaud J, Harchaoui Z, Schmid C. DeepFlow: large displacement optical flow with deep matching. In: Proceedings of the ICCV2013; IEEE Conference Publications, New Jersey; 2013. p. 1385–92.
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Zhang, Z., Huang, F., Tan, L. (2015). Robust Hand Tracker Using Joint Temporal Weighted Histogram Features. In: Wong, W. (eds) Proceedings of the 4th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 355. Springer, Cham. https://doi.org/10.1007/978-3-319-11104-9_24
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DOI: https://doi.org/10.1007/978-3-319-11104-9_24
Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-11104-9
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