Pose Flow

Official implementation of Pose Flow: Efficient Online Pose Tracking .

Results on PoseTrack Challenge validation set:

1. Task2: Multi-Person Pose Estimation (mAP)

Method	Head mAP	Shoulder mAP	Elbow mAP	Wrist mAP	Hip mAP	Knee mAP	Ankle mAP	Total mAP
Detect-and-Track(FAIR)	67.5	70.2	62	51.7	60.7	58.7	49.8	60.6
AlphaPose	66.7	73.3	68.3	61.1	67.5	67.0	61.3	66.5

1. Task3: Pose Tracking (MOTA)

Method	Head MOTA	Shoulder MOTA	Elbow MOTA	Wrist MOTA	Hip MOTA	Knee MOTA	Ankle MOTA	Total MOTA	Total MOTP	Speed(FPS)
Detect-and-Track(FAIR)	61.7	65.5	57.3	45.7	54.3	53.1	45.7	55.2	61.5	Unknown
PoseFlow(DeepMatch)	59.8	67.0	59.8	51.6	60.0	58.4	50.5	58.3	67.8	8
PoseFlow(OrbMatch)	59.0	66.8	60.0	51.8	59.4	58.4	50.3	58.0	62.2	24

Latest Features

• Dec 2018: PoseFlow(General Version) released! Support ANY DATASET and pose tracking results visualization.
• Oct 2018: Support generating correspondence files with ORB(OpenCV), 3X FASTER and no need to compile DeepMatching library.

Requirements

• Python 2.7.13
• OpenCV 3.4.2.16
• OpenCV-contrib 3.4.2.16
• tqdm 4.19.8

Installation

1. Download PoseTrack Dataset from PoseTrack to AlphaPose/PoseFlow/posetrack_data/
2. (Optional) Use DeepMatching to extract dense correspondences between adjcent frames in every video, please refer to DeepMatching Compile Error to compile DeepMatching correctly

pip install -r requirements.txt

cd deepmatching
make clean all
make
cd ..

For Any Datasets (General Version)

1. Using AlphaPose to generate multi-person pose estimation results.

# pytorch version
python demo.py --indir ${image_dir}$ --outdir ${results_dir}$

# torch version
./run.sh --indir ${image_dir}$ --outdir ${results_dir}$

1. Run pose tracking

# pytorch version
python tracker-general.py --imgdir ${image_dir}$ 
                          --in_json ${results_dir}$/alphapose-results.json 
                          --out_json ${results_dir}$/alphapose-results-forvis-tracked.json
                          --visdir ${render_dir}$

# torch version
python tracker-general.py --imgdir ${image_dir}$ 
                          --in_json ${results_dir}$/POSE/alpha-pose-results-forvis.json 
                          --out_json ${results_dir}$/POSE/alpha-pose-results-forvis-tracked.json
                          --visdir ${render_dir}$

For PoseTrack Dataset Evaluation (Paper Baseline)

1. Using AlphaPose to generate multi-person pose estimation results on videos with format like alpha-pose-results-sample.json.
2. Using DeepMatching/ORB to generate correspondence files.

# Generate correspondences by DeepMatching
# (More Robust but Slower)
python matching.py --orb=0 

or

# Generate correspondences by Orb
# (Faster but Less Robust)
python matching.py --orb=1

1. Run pose tracking

python tracker-baseline.py --dataset=val/test  --orb=1/0

1. Evaluation

Original poseval has some instructions on how to convert annotation files from MAT to JSON.

Evaluate pose tracking results on validation dataset:

git clone https://github.com/leonid-pishchulin/poseval.git --recursive
cd poseval/py && export PYTHONPATH=$PWD/../py-motmetrics:$PYTHONPATH
cd ../../
python poseval/py/evaluate.py --groundTruth=./posetrack_data/annotations/val \
                    --predictions=./${track_result_dir}/ \
                    --evalPoseTracking --evalPoseEstimation

Citation

Please cite these papers in your publications if it helps your research:

@inproceedings{xiu2018poseflow,
  author = {Xiu, Yuliang and Li, Jiefeng and Wang, Haoyu and Fang, Yinghong and Lu, Cewu},
  title = {{Pose Flow}: Efficient Online Pose Tracking},
  booktitle={BMVC},
  year = {2018}
}