Poseval

Created by Leonid Pishchulin. Adapted by Sven Kreiss.

Install directly from GitHub:

pip install https://github.com/svenkreiss/poseval.git

Install from a local clone:

git clone https://github.com/svenkreiss/poseval.git
cd poseval
pip install -e .  # install the local package ('.') in editable mode ('-e')

Changes:

• Python 3
• uses latest motmetrics from PyPI (much(!!!) faster); removed git submodule py-motmetrics

Test command with small test data:

python -m poseval.evaluate \
    --groundTruth test_data/gt/ \
    --predictions test_data/pred/ \
    --evalPoseTracking \
    --evalPoseEstimation \
    --saveEvalPerSequence

Lint: pylint poseval.

---

Evaluation of Multi-Person Pose Estimation and Tracking

Created by Leonid Pishchulin

Introduction

This README provides instructions how to evaluate your method's predictions on PoseTrack Dataset locally or using evaluation server.

Prerequisites

• numpy>=1.12.1
• pandas>=0.19.2
• scipy>=0.19.0
• tqdm>=4.24.0
• click>=6.7

Install

$ git clone https://github.com/leonid-pishchulin/poseval.git --recursive
$ cd poseval/py && export PYTHONPATH=$PWD/../py-motmetrics:$PYTHONPATH

Data preparation

Evaluation requires ground truth (GT) annotations available at PoseTrack and your method's predictions. Both GT annotations and your predictions must be saved in json format. Following GT annotations, predictions must be stored per sequence, for each frame of the sequence, using the same structure as GT annotations, and have the same filename as GT annotations. For evaluation on Posetrack 2017, predictions have to follow Posetrack 2017 annotation format, while for evaluation on Posetrack 2018 corresponding 2018 format should be used. Example of json prediction structure for Posetrack 2017 format:

{
   "annolist": [
       {
	   "image": [
	       {
		  "name": "images\/bonn_5sec\/000342_mpii\/00000001.jpg"
	       }
           ],
           "annorect": [
	       {
	           "x1": [625],
		   "y1": [94],
		   "x2": [681],
		   "y2": [178],
		   "score": [0.9],
		   "track_id": [0],
		   "annopoints": [
		       {
			   "point": [
			       {
			           "id": [0],
				   "x": [394],
				   "y": [173],
				   "score": [0.7],
			       },
			       { ... }
			   ]
		       }
		   ]
		},
		{ ... }
	   ],
       },
       { ... }
   ]
}

Note: values of track_id must integers from the interval [0, 999]. For example annotation format of Posetrack 2018 please refer to the corresponding GT annotations.

We provide a possibility to convert a Matlab structure into json format.

$ cd poseval/matlab
$ matlab -nodisplay -nodesktop -r "mat2json('/path/to/dir/with/mat/files/'); quit"

Metrics

This code allows to perform evaluation of per-frame multi-person pose estimation and evaluation of video-based multi-person pose tracking.

Per-frame multi-person pose estimation

Average Precision (AP) metric is used for evaluation of per-frame multi-person pose estimation. Our implementation follows the measure proposed in [1] and requires predicted body poses with body joint detection scores as input. First, multiple body pose predictions are greedily assigned to the ground truth (GT) based on the highest PCKh [3]. Only single pose can be assigned to GT. Unassigned predictions are counted as false positives. Finally, part detection score is used to compute AP for each body part. Mean AP over all body parts is reported as well.

Video-based pose tracking

Multiple Object Tracking (MOT) metrics [2] are used for evaluation of video-based pose tracking. Our implementation builds on the MOT evaluation code [4] and requires predicted body poses with tracklet IDs as input. First, for each frame, for each body joint class, distances between predicted locations and GT locations are computed. Then, predicted tracklet IDs and GT tracklet IDs are taken into account and all (prediction, GT) pairs with distances not exceeding PCKh [3] threshold are considered during global matching of predicted tracklets to GT tracklets for each particular body joint. Global matching minimizes the total assignment distance. Finally, Multiple Object Tracker Accuracy (MOTA), Multiple Object Tracker Precision (MOTP), Precision, and Recall metrics are computed. We report MOTA metric for each body joint class and average over all body joints, while for MOTP, Precision, and Recall we report averages only.

Evaluation (local)

Evaluation code has been tested in Linux and Ubuntu OS. Evaluation takes as input path to directory with GT annotations and path to directory with predictions. See "Data preparation" for details on prediction format.

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

Evaluation of multi-person pose estimation requires joint detection scores, while evaluation of pose tracking requires predicted tracklet IDs per pose.

Evaluation (server)

In order to evaluate using evaluation server, zip your directory containing json prediction files and submit at https://posetrack.net. Shortly you will receive an email containing evaluation results. Prior to submitting your results to evaluation server, make sure you are able to evaluate locally on val set to avoid issues due to incorrect formatting of predictions.

References

[1] DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation. L. Pishchulin, E. Insafutdinov, S. Tang, B. Andres, M. Andriluka, P. Gehler, and B. Schiele. In CVPR'16

[2] Evaluating multiple object tracking performance: the CLEAR MOT metrics. K. Bernardin and R. Stiefelhagen. EURASIP J. Image Vide.'08

[3] 2D Human Pose Estimation: New Benchmark and State of the Art Analysis. M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele. In CVPR'14

[4] https://github.com/cheind/py-motmetrics

For further questions and details, contact PoseTrack Team mailto:[email protected]