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A Botom-Up Approach for Multi-Person Pose Estimation

Building a simple baseline for bottom-up human pose estimation. Models trained on COCO and CrowdPose datasets are available. Welcome to contribute to this project.

Earlier project: SimplePose

Introduction

A bottom-up approach for the problem of multi-person pose estimation

Guiding offsets greedily “connect” the adjacent keypoints belonging to the same persons.

guidding offsets

Overview of the proposed approach

(a): Responses of “left shoulder” (b): Responses of “left hip”

(c): Guiding offsets from “left shoulder” to “left hip” (d): Candidate keypoints and limbs

(e): Greedy keypoint grouping (f): Final result

pipeline

Project Contents

Training Code
Evaluation Code
Image Demo
More (in development)

Project Features

Implement the models using Pytorch in auto mixed-precision (using Nvidia Apex).
Support training on multiple GPUs (over 90% GPU usage rate on each GPU card).
Fast data preparing and augmentation during training.
Focal L2 loss for keypoint heatmap regression.
L1-type loss for guiding offset regression.
Easy to train and run.

Prepare

Install packages according to requirement.txt.

Python=3.6, Pytorch>1.0, Nvidia Apex and other packages needed.
Download the COCO and CrowdPose datasets.
Download the pre-trained models via: GoogleDrive.
Change the paths in the code according to your environment.
Refer to the docs cli-help-evaluate.txt,cli-help-train_dist.txt to know the hypter-parameter settings and more info of this project.
Full project is to be released. Also refer to other branches.

Evaluation (single-scale input)

Set the long side of the input image to 640

Run

python evaluate.py --no-pretrain --initialize-whole False --checkpoint-whole link2checkpoints_storage/PoseNet_77_epoch.pth --resume --sqrt-re --batch-size 8 --loader-workers 4 --thre-hmp 0.06 --topk 32 --headnets hmp omp --dist-max 40 --long-edge 640 --dataset val --flip-test --thre-hmp 0.04 --person-thre 0.04

Hint: if you want to achieve a higher speed (30+ FPS on a 2080 TI), do not use --flip-test

Results on COCO validation dataset

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.661
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.854
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.714
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.622
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.722
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.702
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.873
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.747
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.644
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.787

Run

python evaluate.py --no-pretrain --initialize-whole False --checkpoint-whole link2checkpoints_storage/PoseNet_77_epoch.pth --resume --sqrt-re --batch-size 8 --loader-workers 4 --thre-hmp 0.06 --topk 32 --headnets hmp omp --dist-max 40 --long-edge 640 --dataset test-dev --flip-test --thre-hmp 0.04 --person-thre 0.04

Hint: if you want to achieve a higher speed (30+ FPS on a 2080 TI), do not use --flip-test

Results on COCO test-dev dataset

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.647
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.858
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.705
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.607
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.704
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.696
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.886
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.748
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.636
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.779

Fix the height of the input image to 640

COCO test-dev dataset

python evaluate.py --no-pretrain --initialize-whole False --checkpoint-whole link2checkpoints_storage/PoseNet_77_epoch.pth --resume --sqrt-re --batch-size 8 --loader-workers 4 --thre-hmp 0.06 --topk 32 --headnets hmp omp --dist-max 40 --long-edge 640 --dataset test-dev --flip-test --fixed-height --thre-hmp 0.04 --person-thre 0.04

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.656
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.859
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.713
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.633
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.688
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.702
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.886
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.750
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.659
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.762

CrowdPose test set

Please refer to the develop branch. Change the cofig file to crowdpose setting, then run

python evaluate_crowd.py --no-pretrain --initialize-whole False --checkpoint-whole link2checkpoints_storage_crowdpose/PoseNet_190_epoch.pth --resume --sqrt-re --batch-size 4 --loader-workers 4 --thre-hmp 0.04 --topk 32 --headnets hmp omp --dist-max 40 --long-edge 640 --dataset test  --person-thre 0.02 --flip-test --fixed-height

Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.652
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.859
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.695
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.706
Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.892
Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.743
Average Precision (AP) @[ IoU=0.50:0.95 | type= easy | maxDets= 20 ] = 0.738
Average Precision (AP) @[ IoU=0.50:0.95 | type=medium | maxDets= 20 ] = 0.662
Average Precision (AP) @[ IoU=0.50:0.95 | type= hard | maxDets= 20 ] = 0.548

Training

In our paper, we fine-tune the pre-trained model multi_pose_hg_3x.pth in CenterNet. For simplicity, you can employ our pre-trained models (i.e., training from a checkpoint in GoogleDrive).

Run example:

python -m torch.distributed.launch --nproc_per_node=4 train_dist.py --basenet-checkpoint weights/hourglass_104_renamed.pth --checkpoint-whole link2checkpoints_storage/PoseNet_77_epoch.pth --resume --weight-decay 0 --hmp-loss focal_l2_loss --offset-loss offset_instance_l1_loss --sqrt-re --include-scale --scale-loss scale_l1_loss  --lambdas 1 0 0 10000 10 --headnets hmp omp --learning-rate 1.25e-4 --fgamma 2 --drop-amp-state --drop-optim-state

Acknowledgement

We refer to and borrow some code from SimplePose, OpenPifPaf, CenterNet, etc.

Citation

If this work help your research, please cite the corresponding paper:

@inproceedings{li2020simple,
  title={Simple pose: Rethinking and improving a bottom-up approach for multi-person pose estimation},
  author={Li, Jia and Su, Wen and Wang, Zengfu},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={34},
  number={07},
  pages={11354--11361},
  year={2020}
}

@article{li2021greedy,
  title={Greedy Offset-Guided Keypoint Grouping for Human Pose Estimation},
  author={Li, Jia and Xiang, Linhua and Chen, Jiwei and Wang, Zengfu},
  journal={arXiv preprint arXiv:2107.03098},
  year={2021}
}

@article{li2022multi,
  title={Multi-person pose estimation with accurate heatmap regression and greedy association},
  author={Li, Jia and Wang, Meng},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  volume={32},
  number={8},
  pages={5521--5535},
  year={2022},
  publisher={IEEE}
}