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Proceedings Paper

Hand tracking based on compressed sensing and multiple feature descriptors
Author(s): Yi Zheng; Ping Zheng
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Paper Abstract

Computer vision based interaction between bare hands and virtual objects is an urgent problem to be solved in augmented reality and teleoperation. Bare hand tracking is one of the key issues. An effective hand tracking method based on compressed sensing and multiple feature descriptors is studied in depth. Firstly, a rectangular tracking window containing the hand is determined manually in the initial frame. Using the compressed sensing theory, key Haar feature values and HOG (abbreviation of histogram of oriented gradients) feature values of the initial tracking window are calculated respectively. Thus the classifier is initialized. For the subsequent frames, those positive samples and negative ones around the moving hand are captured, their feature values are calculated, and the classifier is updated. The candidate region corresponding to the maximum of the classifier is taken as the target region of the moving hand in each frame. In the process, Haar feature values and HOG feature values of the candidate region samples are calculated respectively. Simulation experiments and real experiments are carried out by using the proposed tracking method. Experimental results demonstrate that the proposed method can track the moving hand effectively. The proposed hand tracking method can be used in the fields of human computer interaction, augmented reality and teleoperation.

Paper Details

Date Published: 9 August 2018
PDF: 10 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060D (9 August 2018); doi: 10.1117/12.2503287
Show Author Affiliations
Yi Zheng, Shandong Technology and Business Univ. (China)
Ping Zheng, Anhui Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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