A Botom-Up Approach for Multi-Person Pose Estimation

Building a simple baseline for bottom-up human pose estimation. 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.

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

Project Contents

1. Training Code
2. Evaluation Code
3. Image Demo
4. 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.

Prepare

1. Install packages according to requirement.txt.

   Python=3.6, Pytorch>1.0, Nvidia Apex and other packages needed.

2. Download the COCO and CrowdPose datasets.

3. Download the pre-trained models via: GoogleDrive.

4. Change the paths in the code according to your environment.

5. 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.

6. 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

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}
}