# Multi-type_vehicles_flow_statistics

**Repository Path**: dididi_0924/Multi-type_vehicles_flow_statistics

## Basic Information

- **Project Name**: Multi-type_vehicles_flow_statistics
- **Description**: According to YOLOv3 and SORT algorithms, counting multi-type vehicles. Implemented by Pytorch.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-07-02
- **Last Updated**: 2022-02-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Multi-type_vehicles_flow_statistics
According to YOLOv3 and SORT algorithms, counting multi-type vehicles. Implemented by Pytorch.  
Detecting and tracking the vehicles in \["bicycle","bus","car","motorbike","truck"].

## Reference
- yolov3-darknet  https://github.com/pjreddie/darknet
- yolov3-pytorch  https://github.com/eriklindernoren/PyTorch-YOLOv3
- sort https://github.com/abewley/sort

## Dependencies
- ubuntu/windows
- cuda>=10.0
- python>=3.6
- `pip3 install -r requirements.txt`

## Usage
1. Download the pre-trained yolov3 weight file [here](https://pjreddie.com/media/files/yolov3.weights) and put it into `weights` directory;  
2. Run `python3 app.py` ;
3. Select video and double click the image to select area, and then start;
4. After detecting and tracking, the result video and file are saved under `results` directory, the line of `results.txt` with format \[videoName,id,objectName] for each vehicle.

## Demo
![avatar](https://github.com/wsh122333/Multi-type_vehicles_flow_statistics/raw/master/asserts/demo1.gif)

![avatar](https://github.com/wsh122333/Multi-type_vehicles_flow_statistics/raw/master/asserts/demo2.gif)

![avatar](https://github.com/wsh122333/Multi-type_vehicles_flow_statistics/raw/master/asserts/demo3.gif)