https://github.com/mikel-brostrom/yolov3_deepsort_pytorch

Real-time multi-person tracker using YOLO v3 and deep sort
https://github.com/mikel-brostrom/yolov3_deepsort_pytorch

computer-camera deep-association-metric deep-sort http-stream multple-object-tracking pedestrian-tracking pytorch pytorch-yolov3 real-time rtsp-stream simple-online-and-realtime-tracking video web-camera yolo-v3 yolov3 you-only-look-once

Last synced: 8 days ago
JSON representation

Real-time multi-person tracker using YOLO v3 and deep sort

• Host: GitHub
• URL: https://github.com/mikel-brostrom/yolov3_deepsort_pytorch
• Owner: mikel-brostrom
• Created: 2020-02-05T10:03:56.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2022-11-23T12:21:14.000Z (about 2 years ago)
• Last Synced: 2024-12-10T05:42:09.375Z (18 days ago)
• Topics: computer-camera, deep-association-metric, deep-sort, http-stream, multple-object-tracking, pedestrian-tracking, pytorch, pytorch-yolov3, real-time, rtsp-stream, simple-online-and-realtime-tracking, video, web-camera, yolo-v3, yolov3, you-only-look-once
• Language: Python
• Homepage:
• Size: 47.1 MB
• Stars: 168
• Watchers: 4
• Forks: 45
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Yolov3 + Deep Sort with PyTorch

![](yolov3/Town.gif)

## Introduction

This repository contains a moded version of PyTorch YOLOv3 (https://github.com/ultralytics/yolov3). It filters out every detection that is not a person. The detections of persons are then passed to a Deep Sort algorithm (https://github.com/ZQPei/deep_sort_pytorch) which tracks the persons. The reason behind the fact that it just tracks persons is that the deep association metric is trained on a person ONLY datatset.

## Description

The implementation is based on two papers:

- Simple Online and Realtime Tracking with a Deep Association Metric

https://arxiv.org/abs/1703.07402

- YOLOv3: An Incremental Improvement

https://arxiv.org/abs/1804.02767

## Requirements

Python 3.7 or later with all of the `pip install -U -r requirements.txt` packages including:

- `torch >= 1.3`

- `opencv-python`

- `Pillow`

All dependencies are included in the associated docker images. Docker requirements are: 

- `nvidia-docker`

- Nvidia Driver Version >= 440.44

## Before you run the tracker

Github block pushes of files larger than 100 MB (https://help.github.com/en/github/managing-large-files/conditions-for-large-files). Hence the yolo weights needs to be stored somewhere else. When you run tracker.py you will get an exceptions telling you that the yolov3 weight are missing and a link to download them from. Place the downlaoded `.pt` file under `yolov3/weights/`. The weights for deep sort are already in this repo. They can be found under `deep_sort/deep/checkpoint/`.

## Tracking

`track.py` runs tracking on any video source:

```bash

python3 track.py --source ...

```

- Video:  `--source file.mp4`

- Webcam:  `--source 0`

- RTSP stream:  `--source rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa`

- HTTP stream:  `--source http://wmccpinetop.axiscam.net/mjpg/video.mjpg`

## Cite

If you find this project useful in your research, please consider cite:

```latex

@misc{yolov3-deepsort,

    title={Real-time multi-camera multi-object tracker using YOLOv3 and DeepSORT},

    author={Mikel Broström},

    howpublished = {\url{https://github.com/mikel-brostrom/Yolov3_DeepSort_Pytorch}},

    year={2019}

}

```

## Other information

For more detailed information about the algorithms and their corresponding lisences used in this project access their official github implementations.