ByteTrack

Model description

ByteTrack is a simple, fast and strong multi-object tracker.

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods obtain identities by associating detection boxes whose scores are higher than a threshold. The objects with low detection scores, e.g. occluded objects, are simply thrown away, which brings non-negligible true object missing and fragmented trajectories. To solve this problem, we present a simple, effective and generic association method, tracking by associating every detection box instead of only the high score ones. For the low score detection boxes, we utilize their similarities with tracklets to recover true objects and filter out the background detections. When applied to 9 different state-of-the-art trackers, our method achieves consistent improvement on IDF1 scores ranging from 1 to 10 points. To put forwards the state-of-the-art performance of MOT, we design a simple and strong tracker, named ByteTrack. For the first time, we achieve 80.3 MOTA, 77.3 IDF1 and 63.1 HOTA on the test set of MOT17 with 30 FPS running speed on a single V100 GPU.

Step 1: Installation

git clone -b release/2.6 https://github.com/PaddlePaddle/PaddleDetection.git
cd PaddleDetection
pip3 install -r requirements.txt
pip3 install protobuf==3.20.3 
pip3 install urllib3==1.26.6
yum install mesa-libGL
python3 setup.py develop

Step 2: Preparing datasets

Go to visit MOT17 official website, then download the MOT17 dataset, or you can download via paddledet data,then extract and place it in the dataset/mot/folder.

The dataset path structure sholud look like:

datasets/mot/MOT17/
├── annotations
│   ├── train_half.json
│   └── train.json
|   └── val_half.json
└── images
│   ├── train
│   └── half
│   └── test
└── labels_with_ids
│   ├── train

Step 3: Training

# One GPU
CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/mot/bytetrack/detector/ppyoloe_crn_l_36e_640x640_mot17half.yml --eval --amp

# Eight GPUs
python3 -m paddle.distributed.launch --log_dir=ppyoloe --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/mot/bytetrack/detector/ppyoloe_crn_l_36e_640x640_mot17half.yml --eval --amp

Results

Reference

• PaddleDetection