Skip to content

Latest commit

 

History

History

trackers

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Pose Tracking Module for AlphaPose

AlphaPose provide three different tracking methods for now, you can try different method to see which one is better for you.

1. Human-ReID based tracking (Recommended)

Currently the best performance tracking model. Paper coming soon.

Getting started

Download human reid model ( Google drive or Baidu Pan, code:6a8b)and place it into AlphaPose/trackers/weights/.

Then simply run alphapose with additional flag --pose_track

You can try different person reid model by modifing cfg.arch and cfg.loadmodel in ./trackers/tracker_cfg.py.

If you want to train your own reid model, please refer to this project

Demo

./scripts/inference.sh ${CONFIG} ${CHECKPOINT} ${VIDEO_NAME}  ${OUTPUT_DIR}, --pose_track

Todo

  • [] Evaluation Tools for PoseTrack
  • [] More Models
  • [] Training code for PoseTrack Dataset

2. Detector based human tracking

Use a human detecter with tracking module (JDE). Please refer to detector/tracker/

Getting started

Download detector JDE-1088x608 and place it under AlphaPose/detector/tracker/data/

Enable tracking by setting the detector as tracker: --detector tracker

Demo

./scripts/inference.sh ${CONFIG} ${CHECKPOINT} ${VIDEO_NAME}  ${OUTPUT_DIR}, --detector tracker

3. PoseFlow human tracking

This tracker is based on our BMVC 2018 paper PoseFlow, for more info please refer to PoseFlow/README.md

Getting started

Simply run alphapose with additional flag --pose_flow