MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors

This fork from https://github.com/megvii-research/MOTRv2 MOTRv2, and .

Main Results

DanceTrack

HOTA	DetA	AssA	MOTA	IDF1	URL
69.9	83.0	59.0	91.9	71.7	model

Visualization

|VISAM| ||

Installation

The codebase is built on top of Deformable DETR and MOTR.

Requirements

Install pytorch using conda (optional)

conda create -n motrv2 python=3.9
conda activate motrv2
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch

Other requirements
```
pip install -r requirements.txt
```
Build MultiScaleDeformableAttention
```
cd ./models/ops
sh ./make.sh
```

Usage

Dataset preparation

Download YOLOX detection from here.
Please download DanceTrack and CrowdHuman and unzip them as follows:

/data/Dataset/mot
├── crowdhuman
│   ├── annotation_train.odgt
│   ├── annotation_trainval.odgt
│   ├── annotation_val.odgt
│   └── Images
├── DanceTrack
│   ├── test
│   ├── train
│   └── val
├── det_db_motrv2.json

You may use the following command for generating crowdhuman trainval annotation:

cat annotation_train.odgt annotation_val.odgt > annotation_trainval.odgt

Training

You may download the coco pretrained weight from Deformable DETR (+ iterative bounding box refinement), and modify the --pretrained argument to the path of the weight. Then training MOTR on 8 GPUs as following:

./tools/train.sh configs/motrv2.args

Inference on DanceTrack Test Set

Download SAM weigth fro ViT-H SAM model
run

# run a simple inference on our pretrained weights
./tools/simple_inference.sh ./motrv2_dancetrack.pth

# Or evaluate an experiment run
# ./tools/eval.sh exps/motrv2/run1

# then zip the results
zip motrv2.zip tracker/ -r

if you want run on yourself data, please get detection results from ByteTrackInference firstly.

kang-hana/VISAM