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#sportstrack

This repository is an official implementation of the SportsTrack: An Innovative Method for Tracking Athletes in Sports Scenes

1st place of 2022 ECCV Sports MOT challenge.

Result On SportsMOT test

Method HOTA AssA DetA MOTA weight
SportsTrack 76.264 73.538 79.180 89.316 baidu/onedrive

Quick Start

  • Download SportsMOT dataset from here

  • set data root path of sports MOT dataset

export SPORTSTRACK_ROOT=...

ls  ${SPORTSTRACK_ROOT}/data/sportsmot_publish/dataset/test

output

v_1UDUODIBSsc_c001  v_2ChiYdg5bxI_c058  v_2Dw9QNH5KtU_c014  v_7FTsO8S3h88_c007  v_9p0i81kAEwE_c010  v_BdD9xu0E2H4_c011  v_czYZnO9QxYQ_c020
...
  • build gmc

build gmc need opencv4

cd gmc
mkdir build
cd build
cmake ..
make
./gmc ${SPORTSTRACK_ROOT}/data/sportsmot_publish/dataset/test/*   #this will generate a gmc.txt file in each input directorys
./gmc ${SPORTSTRACK_ROOT}/data/sportsmot_publish/dataset/val/*
  • run algorithm
python main.py --split val --gpus 0    #track on val dataset

or

python main.py --split test --gpus 0  #track on test dataset

or use multi process

python multi_process_main.py --split test --gpus 0   #use multiprocess to track on test dataset
  • post process
python postprocess_tools/merge_tracks.py --image_dir ... --input_txt_dir ... --output_txt_dir ...
python postprocess_tools/interpolation.py --input_txt_dir ... --output_txt_dir ...

Example:

 python postprocess_tools/merge_tracks.py --image_dir ${SPORTSTRACK_ROOT}/data/sportsmot_publish/dataset/test --input_txt_dir ${SPORTSTRACK_ROOT}/tmp/sportsmot-test/PDSMV2SportsTrackerT1/data/ --output_txt_dir ${SPORTSTRACK_ROOT}/tmp/sportsmot-test/PDSMV2SportsTrackerT1/data_merge
 python postprocess_tools/interpolation.py --input_txt_dir ${SPORTSTRACK_ROOT}/tmp/sportsmot-test/PDSMV2SportsTrackerT1/data_merge/ --output_txt_dir ${SPORTSTRACK_ROOT}/tmp/sportsmot-test/PDSMV2SportsTrackerT1/data_inter

requirements

install wml

git clone [email protected]:vghost2008/wml.git
cd wml
export PYTHONPATH=${PYTHONPATH}:`pwd`

Team members

  • Jie Wang
  • Xiaodong Yang
  • YuZhou Peng
  • Ting Wang
  • Yanming Zhang

Acknowledge

We acknowledge the excellent implementation from YOLOX , FastReID and HRNet.