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[ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
https://github.com/ifzhang/bytetrack
deployment multi-object-tracking pytorch real-time
Last synced: about 1 month ago
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[ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
Host: GitHub
URL: https://github.com/ifzhang/bytetrack
Owner: ifzhang
License: mit
Created: 2021-08-27T10:47:57.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-06-19T11:45:05.000Z (5 months ago)
Last Synced: 2024-09-26T02:22:46.128Z (about 2 months ago)
Topics: deployment, multi-object-tracking, pytorch, real-time
Language: Python
Homepage:
Size: 77.7 MB
Stars: 4,671
Watchers: 43
Forks: 889
Open Issues: 302
Metadata Files:
- Readme: README.md
- License: LICENSE
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README

        # ByteTrack

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/bytetrack-multi-object-tracking-by-1/multi-object-tracking-on-mot17)](https://paperswithcode.com/sota/multi-object-tracking-on-mot17?p=bytetrack-multi-object-tracking-by-1)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/bytetrack-multi-object-tracking-by-1/multi-object-tracking-on-mot20-1)](https://paperswithcode.com/sota/multi-object-tracking-on-mot20-1?p=bytetrack-multi-object-tracking-by-1)

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



> [**ByteTrack: Multi-Object Tracking by Associating Every Detection Box**](https://arxiv.org/abs/2110.06864)

> 

> Yifu Zhang, Peize Sun, Yi Jiang, Dongdong Yu, Fucheng Weng, Zehuan Yuan, Ping Luo, Wenyu Liu, Xinggang Wang

> 

> *[arXiv 2110.06864](https://arxiv.org/abs/2110.06864)*

## Demo Links

| Google Colab Demo | Huggingface Demo |                  YouTube Tutorial                   | Original Paper: ByteTrack |

|:-----------------:|:----------------:|:---------------------------------------------------:|:-------------------------:|

|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bDilg4cmXFa8HCKHbsZ_p16p0vrhLyu0?usp=sharing)|[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/bytetrack)|[![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://youtu.be/QCG8QMhga9k)|[arXiv 2110.06864](https://arxiv.org/abs/2110.06864) |

* Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio).

## Abstract

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.



## News

* (2022.07) Our paper is accepted by ECCV 2022!

* (2022.06) A [nice re-implementation](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/bytetrack) by Baidu [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)!

## Tracking performance

### Results on MOT challenge test set

| Dataset    |  MOTA | IDF1 | HOTA | MT | ML | FP | FN | IDs | FPS |

|------------|-------|------|------|-------|-------|------|------|------|------|

|MOT17       | 80.3 | 77.3 | 63.1 | 53.2% | 14.5% | 25491 | 83721 | 2196 | 29.6 |

|MOT20       | 77.8 | 75.2 | 61.3 | 69.2% | 9.5%  | 26249 | 87594 | 1223 | 13.7 |

### Visualization results on MOT challenge test set

   

   

## Installation

### 1. Installing on the host machine

Step1. Install ByteTrack.

```shell

git clone https://github.com/ifzhang/ByteTrack.git

cd ByteTrack

pip3 install -r requirements.txt

python3 setup.py develop

```

Step2. Install [pycocotools](https://github.com/cocodataset/cocoapi).

```shell

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

```

Step3. Others

```shell

pip3 install cython_bbox

```

### 2. Docker build

```shell

docker build -t bytetrack:latest .

# Startup sample

mkdir -p pretrained && \

mkdir -p YOLOX_outputs && \

xhost +local: && \

docker run --gpus all -it --rm \

-v $PWD/pretrained:/workspace/ByteTrack/pretrained \

-v $PWD/datasets:/workspace/ByteTrack/datasets \

-v $PWD/YOLOX_outputs:/workspace/ByteTrack/YOLOX_outputs \

-v /tmp/.X11-unix/:/tmp/.X11-unix:rw \

--device /dev/video0:/dev/video0:mwr \

--net=host \

-e XDG_RUNTIME_DIR=$XDG_RUNTIME_DIR \

-e DISPLAY=$DISPLAY \

--privileged \

bytetrack:latest

```

## Data preparation

Download [MOT17](https://motchallenge.net/), [MOT20](https://motchallenge.net/), [CrowdHuman](https://www.crowdhuman.org/), [Cityperson](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md), [ETHZ](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) and put them under /datasets in the following structure:

```

datasets

   |——————mot

   |        └——————train

   |        └——————test

   └——————crowdhuman

   |         └——————Crowdhuman_train

   |         └——————Crowdhuman_val

   |         └——————annotation_train.odgt

   |         └——————annotation_val.odgt

   └——————MOT20

   |        └——————train

   |        └——————test

   └——————Cityscapes

   |        └——————images

   |        └——————labels_with_ids

   └——————ETHZ

            └——————eth01

            └——————...

            └——————eth07

```

Then, you need to turn the datasets to COCO format and mix different training data:

```shell

cd 

python3 tools/convert_mot17_to_coco.py

python3 tools/convert_mot20_to_coco.py

python3 tools/convert_crowdhuman_to_coco.py

python3 tools/convert_cityperson_to_coco.py

python3 tools/convert_ethz_to_coco.py

```

Before mixing different datasets, you need to follow the operations in [mix_xxx.py](https://github.com/ifzhang/ByteTrack/blob/c116dfc746f9ebe07d419caa8acba9b3acfa79a6/tools/mix_data_ablation.py#L6) to create a data folder and link. Finally, you can mix the training data:

```shell

cd 

python3 tools/mix_data_ablation.py

python3 tools/mix_data_test_mot17.py

python3 tools/mix_data_test_mot20.py

```

## Model zoo

### Ablation model

Train on CrowdHuman and MOT17 half train, evaluate on MOT17 half val

| Model    |  MOTA | IDF1 | IDs | FPS |

|------------|-------|------|------|------|

|ByteTrack_ablation [[google]](https://drive.google.com/file/d/1iqhM-6V_r1FpOlOzrdP_Ejshgk0DxOob/view?usp=sharing), [[baidu(code:eeo8)]](https://pan.baidu.com/s/1W5eRBnxc4x9V8gm7dgdEYg) | 76.6 | 79.3 | 159 | 29.6 |

### MOT17 test model

Train on CrowdHuman, MOT17, Cityperson and ETHZ, evaluate on MOT17 train.

* **Standard models**

| Model    |  MOTA | IDF1 | IDs | FPS |

|------------|-------|------|------|------|

|bytetrack_x_mot17 [[google]](https://drive.google.com/file/d/1P4mY0Yyd3PPTybgZkjMYhFri88nTmJX5/view?usp=sharing), [[baidu(code:ic0i)]](https://pan.baidu.com/s/1OJKrcQa_JP9zofC6ZtGBpw) | 90.0 | 83.3 | 422 | 29.6 |

|bytetrack_l_mot17 [[google]](https://drive.google.com/file/d/1XwfUuCBF4IgWBWK2H7oOhQgEj9Mrb3rz/view?usp=sharing), [[baidu(code:1cml)]](https://pan.baidu.com/s/1242adimKM6TYdeLU2qnuRA) | 88.7 | 80.7 | 460 | 43.7 |

|bytetrack_m_mot17 [[google]](https://drive.google.com/file/d/11Zb0NN_Uu7JwUd9e6Nk8o2_EUfxWqsun/view?usp=sharing), [[baidu(code:u3m4)]](https://pan.baidu.com/s/1fKemO1uZfvNSLzJfURO4TQ) | 87.0 | 80.1 | 477 | 54.1 |

|bytetrack_s_mot17 [[google]](https://drive.google.com/file/d/1uSmhXzyV1Zvb4TJJCzpsZOIcw7CCJLxj/view?usp=sharing), [[baidu(code:qflm)]](https://pan.baidu.com/s/1PiP1kQfgxAIrnGUbFP6Wfg) | 79.2 | 74.3 | 533 | 64.5 |

* **Light models**

| Model    |  MOTA | IDF1 | IDs | Params(M) | FLOPs(G) |

|------------|-------|------|------|------|-------|

|bytetrack_nano_mot17 [[google]](https://drive.google.com/file/d/1AoN2AxzVwOLM0gJ15bcwqZUpFjlDV1dX/view?usp=sharing), [[baidu(code:1ub8)]](https://pan.baidu.com/s/1dMxqBPP7lFNRZ3kFgDmWdw) | 69.0 | 66.3 | 531 | 0.90 | 3.99 |

|bytetrack_tiny_mot17 [[google]](https://drive.google.com/file/d/1LFAl14sql2Q5Y9aNFsX_OqsnIzUD_1ju/view?usp=sharing), [[baidu(code:cr8i)]](https://pan.baidu.com/s/1jgIqisPSDw98HJh8hqhM5w) | 77.1 | 71.5 | 519 | 5.03 | 24.45 |

### MOT20 test model

Train on CrowdHuman and MOT20, evaluate on MOT20 train.

| Model    |  MOTA | IDF1 | IDs | FPS |

|------------|-------|------|------|------|

|bytetrack_x_mot20 [[google]](https://drive.google.com/file/d/1HX2_JpMOjOIj1Z9rJjoet9XNy_cCAs5U/view?usp=sharing), [[baidu(code:3apd)]](https://pan.baidu.com/s/1bowJJj0bAnbhEQ3_6_Am0A) | 93.4 | 89.3 | 1057 | 17.5 |

## Training

The COCO pretrained YOLOX model can be downloaded from their [model zoo](https://github.com/Megvii-BaseDetection/YOLOX/tree/0.1.0). After downloading the pretrained models, you can put them under /pretrained.

* **Train ablation model (MOT17 half train and CrowdHuman)**

```shell

cd 

python3 tools/train.py -f exps/example/mot/yolox_x_ablation.py -d 8 -b 48 --fp16 -o -c pretrained/yolox_x.pth

```

* **Train MOT17 test model (MOT17 train, CrowdHuman, Cityperson and ETHZ)**

```shell

cd 

python3 tools/train.py -f exps/example/mot/yolox_x_mix_det.py -d 8 -b 48 --fp16 -o -c pretrained/yolox_x.pth

```

* **Train MOT20 test model (MOT20 train, CrowdHuman)**

For MOT20, you need to clip the bounding boxes inside the image.

Add clip operation in [line 134-135 in data_augment.py](https://github.com/ifzhang/ByteTrack/blob/72cd6dd24083c337a9177e484b12bb2b5b3069a6/yolox/data/data_augment.py#L134), [line 122-125 in mosaicdetection.py](https://github.com/ifzhang/ByteTrack/blob/72cd6dd24083c337a9177e484b12bb2b5b3069a6/yolox/data/datasets/mosaicdetection.py#L122), [line 217-225 in mosaicdetection.py](https://github.com/ifzhang/ByteTrack/blob/72cd6dd24083c337a9177e484b12bb2b5b3069a6/yolox/data/datasets/mosaicdetection.py#L217), [line 115-118 in boxes.py](https://github.com/ifzhang/ByteTrack/blob/72cd6dd24083c337a9177e484b12bb2b5b3069a6/yolox/utils/boxes.py#L115).

```shell

cd 

python3 tools/train.py -f exps/example/mot/yolox_x_mix_mot20_ch.py -d 8 -b 48 --fp16 -o -c pretrained/yolox_x.pth

```

* **Train custom dataset**

First, you need to prepare your dataset in COCO format. You can refer to [MOT-to-COCO](https://github.com/ifzhang/ByteTrack/blob/main/tools/convert_mot17_to_coco.py) or [CrowdHuman-to-COCO](https://github.com/ifzhang/ByteTrack/blob/main/tools/convert_crowdhuman_to_coco.py). Then, you need to create a Exp file for your dataset. You can refer to the [CrowdHuman](https://github.com/ifzhang/ByteTrack/blob/main/exps/example/mot/yolox_x_ch.py) training Exp file. Don't forget to modify get_data_loader() and get_eval_loader in your Exp file. Finally, you can train bytetrack on your dataset by running:

```shell

cd 

python3 tools/train.py -f exps/example/mot/your_exp_file.py -d 8 -b 48 --fp16 -o -c pretrained/yolox_x.pth

```

## Tracking

* **Evaluation on MOT17 half val**

Run ByteTrack:

```shell

cd 

python3 tools/track.py -f exps/example/mot/yolox_x_ablation.py -c pretrained/bytetrack_ablation.pth.tar -b 1 -d 1 --fp16 --fuse

```

You can get 76.6 MOTA using our pretrained model.

Run other trackers:

```shell

python3 tools/track_sort.py -f exps/example/mot/yolox_x_ablation.py -c pretrained/bytetrack_ablation.pth.tar -b 1 -d 1 --fp16 --fuse

python3 tools/track_deepsort.py -f exps/example/mot/yolox_x_ablation.py -c pretrained/bytetrack_ablation.pth.tar -b 1 -d 1 --fp16 --fuse

python3 tools/track_motdt.py -f exps/example/mot/yolox_x_ablation.py -c pretrained/bytetrack_ablation.pth.tar -b 1 -d 1 --fp16 --fuse

```

* **Test on MOT17**

Run ByteTrack:

```shell

cd 

python3 tools/track.py -f exps/example/mot/yolox_x_mix_det.py -c pretrained/bytetrack_x_mot17.pth.tar -b 1 -d 1 --fp16 --fuse

python3 tools/interpolation.py

```

Submit the txt files to [MOTChallenge](https://motchallenge.net/) website and you can get 79+ MOTA (For 80+ MOTA, you need to carefully tune the test image size and high score detection threshold of each sequence).

* **Test on MOT20**

We use the input size 1600 x 896 for MOT20-04, MOT20-07 and 1920 x 736 for MOT20-06, MOT20-08. You can edit it in [yolox_x_mix_mot20_ch.py](https://github.com/ifzhang/ByteTrack/blob/main/exps/example/mot/yolox_x_mix_mot20_ch.py)

Run ByteTrack:

```shell

cd 

python3 tools/track.py -f exps/example/mot/yolox_x_mix_mot20_ch.py -c pretrained/bytetrack_x_mot20.pth.tar -b 1 -d 1 --fp16 --fuse --match_thresh 0.7 --mot20

python3 tools/interpolation.py

```

Submit the txt files to [MOTChallenge](https://motchallenge.net/) website and you can get 77+ MOTA (For higher MOTA, you need to carefully tune the test image size and high score detection threshold of each sequence).

## Applying BYTE to other trackers

See [tutorials](https://github.com/ifzhang/ByteTrack/tree/main/tutorials).

## Combining BYTE with other detectors

Suppose you have already got the detection results 'dets' (x1, y1, x2, y2, score) from other detectors, you can simply pass the detection results to BYTETracker (you need to first modify some post-processing code according to the format of your detection results in [byte_tracker.py](https://github.com/ifzhang/ByteTrack/blob/main/yolox/tracker/byte_tracker.py)):

```

from yolox.tracker.byte_tracker import BYTETracker

tracker = BYTETracker(args)

for image in images:

   dets = detector(image)

   online_targets = tracker.update(dets, info_imgs, img_size)

```

You can get the tracking results in each frame from 'online_targets'. You can refer to [mot_evaluators.py](https://github.com/ifzhang/ByteTrack/blob/main/yolox/evaluators/mot_evaluator.py) to pass the detection results to BYTETracker.

## Demo



```shell

cd 

python3 tools/demo_track.py video -f exps/example/mot/yolox_x_mix_det.py -c pretrained/bytetrack_x_mot17.pth.tar --fp16 --fuse --save_result

```

## Deploy

1.  [ONNX export and ONNXRuntime](./deploy/ONNXRuntime)

2.  [TensorRT in Python](./deploy/TensorRT/python)

3.  [TensorRT in C++](./deploy/TensorRT/cpp)

4.  [ncnn in C++](./deploy/ncnn/cpp)

5.  [Deepstream](./deploy/DeepStream)

## Citation

```

@article{zhang2022bytetrack,

  title={ByteTrack: Multi-Object Tracking by Associating Every Detection Box},

  author={Zhang, Yifu and Sun, Peize and Jiang, Yi and Yu, Dongdong and Weng, Fucheng and Yuan, Zehuan and Luo, Ping and Liu, Wenyu and Wang, Xinggang},

  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},

  year={2022}

}

```

## Acknowledgement

A large part of the code is borrowed from [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX), [FairMOT](https://github.com/ifzhang/FairMOT), [TransTrack](https://github.com/PeizeSun/TransTrack) and [JDE-Cpp](https://github.com/samylee/Towards-Realtime-MOT-Cpp). Many thanks for their wonderful works.