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PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model

  • George PapandreouEmail author
  • Tyler Zhu
  • Liang-Chieh Chen
  • Spyros Gidaris
  • Jonathan Tompson
  • Kevin Murphy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11218)

Abstract

We present a box-free bottom-up approach for the tasks of pose estimation and instance segmentation of people in multi-person images using an efficient single-shot model. The proposed PersonLab model tackles both semantic-level reasoning and object-part associations using part-based modeling. Our model employs a convolutional network which learns to detect individual keypoints and predict their relative displacements, allowing us to group keypoints into person pose instances. Further, we propose a part-induced geometric embedding descriptor which allows us to associate semantic person pixels with their corresponding person instance, delivering instance-level person segmentations. Our system is based on a fully-convolutional architecture and allows for efficient inference, with runtime essentially independent of the number of people present in the scene. Trained on COCO data alone, our system achieves COCO test-dev keypoint average precision of 0.665 using single-scale inference and 0.687 using multi-scale inference, significantly outperforming all previous bottom-up pose estimation systems. We are also the first bottom-up method to report competitive results for the person class in the COCO instance segmentation task, achieving a person category average precision of 0.417.

Keywords

Person detection and pose estimation Segmentation and grouping 

Supplementary material

474202_1_En_17_MOESM1_ESM.pdf (135 kb)
Supplementary material 1 (pdf 135 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • George Papandreou
    • 1
    Email author
  • Tyler Zhu
    • 1
  • Liang-Chieh Chen
    • 1
  • Spyros Gidaris
    • 1
  • Jonathan Tompson
    • 1
  • Kevin Murphy
    • 1
  1. 1.Google ResearchLos AngelesUSA

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