24 March 2017 Accurate per-pixel hand detection from a single depth image
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Per-pixel hand detection plays an important role in many human–computer interaction applications while accurate and robust hand detection remains a challenging task due to the large appearance variance of hands in images. We introduce a per-pixel hand detection system using one single depth image. We propose a circle sampling depth-context feature for hand regions representation, and a multilayered hand detection model is built for hand regions detection. Finally, a postprocessing step based on spatial constraints is applied to refine the detection results and further improve the detection accuracy. We evaluate the accuracy of our method on a public dataset and investigate the effect of key parameters in our system. The results of the qualitative and quantitative evaluation reveal that the proposed method performs well on per-pixel hand detection tasks. Furthermore, an additional experiment on hand parts segmentation proves that the depth-context feature has a generalization power for more complex multiclass classification tasks.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Zhao Zhang, Zhao Zhang, Zhijiang Zhang, Zhijiang Zhang, Haiwei Zhang, Haiwei Zhang, Dan Zeng, Dan Zeng, } "Accurate per-pixel hand detection from a single depth image," Optical Engineering 56(3), 033107 (24 March 2017). https://doi.org/10.1117/1.OE.56.3.033107 . Submission: Received: 26 November 2016; Accepted: 9 March 2017
Received: 26 November 2016; Accepted: 9 March 2017; Published: 24 March 2017


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