[1]SHI Dongcheng,NI Kang.Motion gesture tracking based on compressed sensing W-HOG features[J].CAAI Transactions on Intelligent Systems,2016,11(1):124-128.[doi:10.11992/tis.201507005]
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Motion gesture tracking based on compressed sensing W-HOG features

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 1 Page number: 124-128 Column: 学术论文—机器感知与模式识别 Public date: 2016-02-25
Title:
Motion gesture tracking based on compressed sensing W-HOG features
Author(s):
SHI Dongcheng;  NI Kang
College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
Keywords:
compressed sensing; Harr features; HOG features; gesture tracking; tracking drifting
CLC:
TP391.9
DOI:
10.11992/tis.201507005
Abstract:
The use of a single or simple feature for gesture tracking always induces tracking errors. To improve the accuracy of hand tracking, this study uses compressed-sensing motion tracking to track a hand and extracts HOG features in the movement area instead of original generalized class Harr features to track the target. At the same time, to reduce the accumulation of errors of gesture tracking generated by weight series, such as HOG features of different image blocks, the study calculates the HOG feature weight and effectively integrates the weight with HOG features to form W-HOG compression characteristics. The statistical experimental results show that the improved algorithm provided increased accuracy by approximately 16% compared with CT algorithm and approximately 6% compared with HOG-CT algorithm. Moreover, the algorithm can accurately detect the moving gesture in a complex background, improve the tracking robustness of gesture tracking in circumstances of illumination changes and background objects with a color similar to the skin, and reduce the occurrence of gesture tracking drifting.
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