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CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark

Citation

If you find our works useful in your reasearch, please consider citing:

@article{li2018crowdpose,
  title={CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark},
  author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},
  journal={arXiv preprint arXiv:1812.00324},
  year={2018}
}

Introduction

This is the official repo of CVPR2019 paper CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark. Our proposed method surpasses the state-of-the-art methods on CrowdPose dataset by 5 mAP and results on MSCOCO dataset demonstrate the generalization ability of our method (comparatively 0.8 mAP higher). Images in our proposed CrowdPose dataset have a uniform distribution of Crowd Index among [0, 1].

Code

We provide evaluation tools for CrowdPose dataset. Our evaluation tools is developed based on @cocodataset/cocoapi. The source code of our model has been integrated into AlphaPose.

Dataset

Train + Validation + Test Images (Google Drive)

Annotations (Google Drive)

Results

Results on CrowdPose Validation:

Compare with state-of-the-art methods

Method AP @0.5:0.95 AP @0.5 AP @0.75 AR @0.5:0.95 AR @0.5 AR @0.75
Detectron (Mask R-CNN) 57.2 83.5 60.3 65.9 89.3 69.4
Simple Pose (Xiao et al.) 60.8 81.4 65.7 67.3 86.3 71.8
Ours 66.0 84.2 71.5 72.7 89.5 77.5

Compare with open-source systems

Method AP @Easy AP @Medium AP @Hard FPS
OpenPose (CMU-Pose) 62.7 48.7 32.3 5.3
Detectron (Mask R-CNN) 69.4 57.9 45.8 2.9
Ours 75.5 66.3 57.4 10.1

Results on MSCOCO Validation:

Method AP @0.5:0.95 AR @0.5:0.95
Detectron (Mask R-CNN) 64.8 71.1
Simple Pose (Xiao et al.) 69.8 74.1
AlphaPose 70.9 76.4

Contributors

CrowdPose is authored by Jiefeng Li, Can Wang, Hao Zhu, Yihuan Mao, Hao-Shu Fang, and Cewu Lu.

About

CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark, CVPR 2019, Oral

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