vehicle_tracking_yolov3: This is the final project of the machine vision course. Given that multi-object tracking (MOT) is currently the research focus, this project will open source to help related research. The basis of this project is: yolo v3, multi-target recognition algorithm, Kalman filter tracking, Hungarian algorithm. (IPIL 2016) This project has basically completed the identification and tracking of vehicle targets in the video, but the robustness is not perfect.

## Introduction
## vehicle recognition and tracking based on yolo v3 and sort algorithem.
This is the final project of the machine vision course. Given that multi-object tracking (MOT) is currently the research focus, this project will open source to help related research. The basis of this project is: **YOLOv3**, multi-target recognition algorithm, Kalman filter tracking, Hungarian algorithm. (IPIL 2016) This project has basically completed the identification and tracking of vehicle targets in the video, but the robustness is not perfect.The yolov3 implementation is from [darknet](https://github.com/pjreddie/darknet). BTW, this project can work well in online network.

# To run this project you need:
- python 3.6
- numpy
- scipy
- opencv-python
- sklearn
- pytorch 0.4 or 1.x
- time
- filterpy
- matplotlib

all of these is load on the request.txt.

# Quick start:

1. Clone this file
```bash
$ git clone https://github.com/Github-chenyang/vehicle_tracking_yolov3.git
```

2. You are supposed to install some dependencies before getting out hands with these codes.

```bash
$ cd vehicle_tracking_yolov3
$ pip install -r ./docs/request.txt
```

for user in china, you can specify pypi source to accelerate install like:
```bash
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple 
```

3. Download YOLOv3 parameters
```bash
$ cd config/
$ wget https://pjreddie.com/media/files/yolov3.weights
$ cd ..
可以到这下载
https://download.csdn.net/download/weixin_32759777/12689462

```

4. Run demo

just run the object_tracker.py and you can realize a simple offline MOA.
here is the effect of the algorithm
![image](https://github.com/Github-chenyang/vehicle_tracking_yolov3/raw/master/docs/1.png)

# train your own cfg as well as weights

This part is mainly based on [packyan’s work](https://github.com/packyan/PyTorch-YOLOv3-kitti). I just change some code so that it is easy for me to use.

##### Download pretrained weights

if you wan use pretrained darknet-53 on IMAGENET weights, please download [darknet53.conv.74](https://pjreddie.com/media/files/darknet53.conv.74),and put it into `checkpoints/`
if you just want a pretrained weights on kitti dataset for test or detect, please download [pretrained weights file](https://drive.google.com/file/d/1BRJDDCMRXdQdQs6-x-3PmlzcEuT9wxJV/view?usp=sharing), and put it into `weights` folder, the path:
`weights/yolov3-kitti.weights`

## if you want some proper vehicle dataset:

I truly recommend the NVIDIA [city dataset](https://www.aicitychallenge.org/2020-data-and-evaluation/).Data for this challenge comes from multiple traffic cameras from a city in the United States as well as from state highways in Iowa. 
maybe this is its looklike.
![image](https://github.com/Github-chenyang/vehicle_tracking_yolov3/raw/master/docs/2.png)

# final

the project has been updated and the performance is better now!
just enjoy!

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