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Human body segmentation based on shape constraint

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Abstract

Human body segmentation is essential for many practical applications, e.g., video surveillance analysis in intelligent urban. However, existing methods mainly suffer from various human poses. In this paper, we try to address this issue by introducing human shape constraint. First, human pose estimation is performed, and locations of human body parts are determined. Contrast to the previous work, we just use the human body parts with high precision. Then we combines the star convexity and the human body parts’ locations as shape constraint. The final segmentation results are acquired through the optimization step. Comprehensive and comparative experimental results demonstrate that the proposed method achieves promising performance and outperforms many state-of-the-art methods over publicly available challenging datasets.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 61402428, 61672475, 61602430); Qingdao Science and Technology Development Plan (No. 16-5-1-13-jch).

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Authors and Affiliations

  1. College of Information Science and Engineering, Ocean University of China, Qingdao, China

    Lei Huang & Zhiqiang Wei

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Jie Nie

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  1. Lei Huang
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  2. Jie Nie
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  3. Zhiqiang Wei
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Correspondence to Zhiqiang Wei.

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Huang, L., Nie, J. & Wei, Z. Human body segmentation based on shape constraint. Machine Vision and Applications 28, 715–724 (2017). https://doi.org/10.1007/s00138-017-0829-3

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