Automatic segmentation of human depth map based on semantic segmentation of FCN and depth segmentation

Ruifeng Yuan; Mei Hui; Ming Liu; Yuejin Zhao; Liquan Dong; Lingqin Kong; Ming Chang; Zhi Cai

doi:10.1117/12.2502880

9 August 2018 Automatic segmentation of human depth map based on semantic segmentation of FCN and depth segmentation

Ruifeng Yuan, Mei Hui, Ming Liu, Yuejin Zhao, Liquan Dong, Lingqin Kong, Ming Chang, Zhi Cai

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108062R (2018) https://doi.org/10.1117/12.2502880
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

Traditional 3D information acquisition of human body relies on either foreground extraction or threshold segmentation in a plain background. It is difficult to be applied directly in complex background. In this paper, a novel method is proposed on the basis of binocular vision, which combines the semantic segmentation of FCN with the depth segmentation to get the human body depth map. The depth map is obtained by binocular camera, and each point in the depth map corresponds to the point in the left camera image. The position of the human body is gained through semantic segmentation of the left camera image, then automatic depth segmentation can be conducted based on the depth of human body in the depth map. The final result is obtained by taking the intersection of the depth map segmentation result and the left camera image segmentation result. The results show that the segmentation precision is much higher than that of purely semantic segmentation of FCN, the segmentation accuracy has increased about 2%.

Citation Download Citation

Ruifeng Yuan, Mei Hui, Ming Liu, Yuejin Zhao, Liquan Dong, Lingqin Kong, Ming Chang, and Zhi Cai "Automatic segmentation of human depth map based on semantic segmentation of FCN and depth segmentation", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108062R (9 August 2018); https://doi.org/10.1117/12.2502880

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KEYWORDS

Image segmentation

Cameras

3D acquisition

RGB color model

3D metrology

Clouds

Image processing algorithms and systems

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