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Multiple-Branches Faster RCNN for Human Parts Detection and Pose Estimation

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Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10118))

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Abstract

In this work, we primarily address multiple people pose estimation challenge by exploring the performance of Faster RCNN on human parts detection. We develop a multiple-branches Faster RCNN model for our specific task of detecting persons and their parts. Our model can improve the performance of detecting human parts and the whole persons, meanwhile speeding up detection process with shared weights. A part-based method is proposed to estimate multiple people poses, bringing recent advances on object detection to this task. Experiments demonstrate that our model achieves better performance than the original Faster RCNN model on our task. Compared with other pose estimation approaches, our approach achieves fair or better results.

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Acknowledgement

We gratefully acknowledge that this work is supported by the fundings from National Natural Science Foundation of China (61273285, 61375019).

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Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China

    Kaiqiang Wei & Xu Zhao

Authors
  1. Kaiqiang Wei
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  2. Xu Zhao
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Corresponding author

Correspondence to Xu Zhao .

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Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Wei, K., Zhao, X. (2017). Multiple-Branches Faster RCNN for Human Parts Detection and Pose Estimation. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_33

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  • DOI: https://doi.org/10.1007/978-3-319-54526-4_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54525-7

  • Online ISBN: 978-3-319-54526-4

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