RethMOT

Rethinking Two-Stage Data Association for Multiple Object Tracking in Crowd Scenes

Ruonan Wei, Yuehuan Wang, and Jinpu Zhang

This code is based on the implementation of ByteTrack, BoT-SORT,

Installation

Setup with Anaconda

Step 1. Install torch and matched torchvision from pytorch.org.
The code was tested using torch 1.11.0+cu113 and torchvision==0.12.0

Step 2. Install RethMOT.

cd RethMOT
pip3 install -r requirements.txt
python3 setup.py develop

Step 3. Install pycocotools.

pip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

Step 4. Others

# Cython-bbox
pip3 install cython_bbox

# faiss cpu / gpu
pip3 install faiss-cpu
pip3 install faiss-gpu

Data Preparation

Download MOT17 and MOT20 from the official website. And put them in the following structure:

<dataets_dir>
      │
      ├── MOT17
      │      ├── train
      │      └── test    
      │
      └── MOT20
             ├── train
             └── test

Tracking

Tuning the tracking parameters carefully could lead to higher performance.

• Test on MOT17

cd <RethMOT_dir>
python3 tools/track.py <dataets_dir/MOT17> --default-parameters --with-reid --benchmark "MOT17" --eval "test" --fp16 --fuse
python3 tools/interpolation.py --txt_path <path_to_track_result>

• Test on MOT20

cd <RethMOT_dir>
python3 tools/track.py <dataets_dir/MOT20> --default-parameters --with-reid --benchmark "MOT20" --eval "test" --fp16 --fuse
python3 tools/interpolation.py --txt_path <path_to_track_result>

• Evaluation on MOT17 validation set (the second half of the train set)

cd <RethMOT_dir>
python3 tools/track.py <dataets_dir/MOT17> --default-parameters --benchmark "MOT17" --eval "val" --fp16 --fuse
# or
python3 tools/track.py <dataets_dir/MOT17> --default-parameters --with-reid --benchmark "MOT17" --eval "val" --fp16 --fuse

Acknowledgement

A large part of the codes, ideas and results are borrowed from ByteTrack, BoT-SORT, StrongSORT, FastReID, YOLOX and YOLOv7. Thanks for their excellent work!