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https://github.com/JackWoo0831/Yolov7-tracker

Yolo X, v7, v8 and several Multi-Object Tracker(SORT, DeepSORT, ByteTrack, BoT-SORT, etc.) in MOT17 and VisDrone2019 Dataset. It uses a unified style and integrated tracker for easy embedding in your own projects.
https://github.com/JackWoo0831/Yolov7-tracker

pytorch

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Yolo X, v7, v8 and several Multi-Object Tracker(SORT, DeepSORT, ByteTrack, BoT-SORT, etc.) in MOT17 and VisDrone2019 Dataset. It uses a unified style and integrated tracker for easy embedding in your own projects.

Awesome Lists containing this project

README 

        
# YOLO detector and SOTA Multi-object tracker Toolbox



## ❗❗Important Notes



Compared to the previous version, this is an ***entirely new version (branch v2)***!!!



**Please use this version directly, as I have almost rewritten all the code to ensure better readability and improved results, as well as to correct some errors in the past code.**



```bash 

git clone https://github.com/JackWoo0831/Yolov7-tracker.git

git checkout v2  # change to v2 branch !!

```



🙌 ***If you have any suggestions for adding trackers***, please leave a comment in the Issues section with the paper title or link! Everyone is welcome to contribute to making this repo better.






**Language**: English | [简体中文](README_CN.md)






## 🗺️ Latest News



- ***2024.11.29*** Fix bugs of C-BIoU Track (the state prediction and updating bugs)

- ***2024.10.24*** Add Hybrid SORT and fix some errors and bugs of OC-SORT.



## ❤️ Introduction



This repo is a toolbox that implements the **tracking-by-detection paradigm multi-object tracker**. The detector supports:



- YOLOX 

- YOLO v7

- YOLO v8, 



and the tracker supports:



- SORT

- DeepSORT 

- ByteTrack ([ECCV2022](https://arxiv.org/pdf/2110.06864))

- Bot-SORT ([arxiv2206](https://arxiv.org/pdf/2206.14651.pdf))

- OCSORT ([CVPR2023](https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_Observation-Centric_SORT_Rethinking_SORT_for_Robust_Multi-Object_Tracking_CVPR_2023_paper.pdf))

- C_BIoU Track ([arxiv2211](https://arxiv.org/pdf/2211.14317v2.pdf))

- Strong SORT ([IEEE TMM 2023](https://arxiv.org/pdf/2202.13514))

- Sparse Track ([arxiv 2306](https://arxiv.org/pdf/2306.05238))

- UCMC Track ([AAAI 2024](http://arxiv.org/abs/2312.08952))

- Hybrid SORT([AAAI 2024](https://ojs.aaai.org/index.php/AAAI/article/view/28471))



and the reid model supports:



- OSNet

- Extractor from DeepSort



The highlights are:

- Supporting more trackers than MMTracking

- Rewrite multiple trackers with a ***unified code style***, without the need to configure multiple environments for each tracker 

- Modular design, which ***decouples*** the detector, tracker, reid model and Kalman filter for easy conducting experiments



![gif](figure/demo.gif)



##  🔨 Installation



The basic env is:

- Ubuntu 18.04

- Python：3.9, Pytorch: 1.12



Run following commond to install other packages:



```bash

pip3 install -r requirements.txt

```



### 🔍 Detector installation



1. YOLOX:



The version of YOLOX is **0.1.0 (same as ByteTrack)**. To install it, you can clone the ByteTrack repo somewhere, and run:



``` bash

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



python3 setup.py develop

```



2. YOLO v7:



There is no need to execute addtional steps as the repo itself is based on YOLOv7.



3. YOLO v8:



Please run:



```bash

pip3 install ultralytics==8.0.94

```



### 📑 Data preparation



***If you do not want to test on the specific dataset, instead, you only want to run demos, please skip this section.***



***No matter what dataset you want to test, please organize it in the following way (YOLO style):***



```

dataset_name

     |---images

           |---train

                 |---sequence_name1

                             |---000001.jpg

                             |---000002.jpg ...

           |---val ...

           |---test ...



     |



```



You can refer to the codes in `./tools` to see how to organize the datasets.



***Then, you need to prepare a `yaml` file to indicate the path so that the code can find the images.***



Some examples are in `tracker/config_files`. The important keys are:



```

DATASET_ROOT: '/data/xxxx/datasets/MOT17'  # your dataset root

SPLIT: test  # train, test or val

CATEGORY_NAMES:  # same in YOLO training

  - 'pedestrian'



CATEGORY_DICT:

  0: 'pedestrian'

```



## 🚗 Practice 



### 🏃 Training 



Trackers generally do not require parameters to be trained. Please refer to the training methods of different detectors to train YOLOs.



Some references may help you:



- YOLOX: `tracker/yolox_utils/train_yolox.py`



- YOLO v7:



```shell

python train_aux.py --dataset visdrone --workers 8 --device <$GPU_id$> --batch-size 16 --data data/visdrone_all.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights <$YOLO v7 pretrained model path$> --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml

```  



- YOLO v8: `tracker/yolov8_utils/train_yolov8.py`



### 😊 Tracking ! 



If you only want to run a demo:



```bash

python tracker/track_demo.py --obj ${video path or images folder path} --detector ${yolox, yolov8 or yolov7} --tracker ${tracker name} --kalman_format ${kalman format, sort, byte, ...} --detector_model_path ${detector weight path} --save_images

```



For example:



```bash

python tracker/track_demo.py --obj M0203.mp4 --detector yolov8 --tracker deepsort --kalman_format byte --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt --save_images

```



If you want to run trackers on dataset:



```bash

python tracker/track.py --dataset ${dataset name, related with the yaml file} --detector ${yolox, yolov8 or yolov7} --tracker ${tracker name} --kalman_format ${kalman format, sort, byte, ...} --detector_model_path ${detector weight path}

```



For example:



- SORT: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker sort --kalman_format sort --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt `



- DeepSORT: `python tracker/track.py --dataset uavdt --detector yolov7 --tracker deepsort --kalman_format byte --detector_model_path weights/yolov7_UAVDT_35epochs_20230507.pt`



- ByteTrack: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker bytetrack --kalman_format byte --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt`



- OCSort: `python tracker/track.py --dataset mot17 --detector yolox --tracker ocsort --kalman_format ocsort --detector_model_path weights/bytetrack_m_mot17.pth.tar`



- C-BIoU Track: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker c_bioutrack --kalman_format bot --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt`



- BoT-SORT: `python tracker/track.py --dataset uavdt --detector yolox --tracker botsort --kalman_format bot --detector_model_path weights/yolox_m_uavdt_50epochs.pth.tar`



- Strong SORT: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker strongsort --kalman_format strongsort --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt`



- Sparse Track: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker sparsetrack --kalman_format bot --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt`



- UCMC Track: `python tracker/track.py --dataset mot17 --detector yolox --tracker ucmctrack --kalman_format ucmc --detector_model_path weights/bytetrack_m_mot17.pth.tar --camera_parameter_folder ./tracker/cam_param_files`



- Hybrid SORT: `python tracker/track.py --dataset mot17 --detector yolox --tracker hybridsort --kalman_format hybridsort --detector_model_path weights/bytetrack_m_mot17.pth.tar --save_images`



> **Important notes for UCMC Track:**

> 

> 1. Camera parameters. The UCMC Track need the intrinsic and extrinsic parameter of camera. Please organize like the format of `tracker/cam_param_files/uavdt/M0101.txt`. One video sequence corresponds to one txt file. If you do not have the labelled parameters, you can refer to the estimating toolbox in original repo ([https://github.com/corfyi/UCMCTrack](https://github.com/corfyi/UCMCTrack)).

> 

> 2. The code does not contain the camera motion compensation part between every two frame, please refer to [https://github.com/corfyi/UCMCTrack/issues/12](https://github.com/corfyi/UCMCTrack/issues/12). From my perspective, since the algorithm name is 'uniform', the update of compensation between every two frames is not necessary.



### ✅ Evaluation 



Coming Soon. As an alternative, after obtaining the result txt file, you can use the [Easier to use TrackEval repo](https://github.com/JackWoo0831/Easier_To_Use_TrackEval).