eplatero97 / Object-Detection-and-Tracking-with-YOLOv3-SORT

YOLOv3 + SORT - Person Counter

This project is to detect and track person on a video stream and count those going through a defined line.

Requirement

• Python 3.5 or higher
• OpenCV
• Numpy
• Numba
• imutils
• filterpy
• Scipy (or sklearn version below 0.23.0)

pip install -r requirements.txt

It uses:

• YOLOv3 to detect objects on each of the video frames. - 用自己的数据训练 YOLOv3 模型

• SORT to track those objects over different frames.

Once the objects are detected and tracked over different frames a simple mathematical calculation is applied to count the intersections between the vehicles previous and current frame positions with a defined line.

Quick Start

1. Download the code to your computer.
2. Download [yolov3.weights] and place it in yolov3_sort/yolo-obj/
3. Run the yolov3 counter:

$ python3 main.py --input input/test.mp4 --output output/test.avi --yolo yolo-obj

Citation

YOLOv3 :

@article{yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal = {arXiv},
year={2018}
}

SORT :

@inproceedings{Bewley2016_sort,
  author={Bewley, Alex and Ge, Zongyuan and Ott, Lionel and Ramos, Fabio and Upcroft, Ben},
  booktitle={2016 IEEE International Conference on Image Processing (ICIP)},
  title={Simple online and realtime tracking},
  year={2016},
  pages={3464-3468},
  keywords={Benchmark testing;Complexity theory;Detectors;Kalman filters;Target tracking;Visualization;Computer Vision;Data Association;Detection;Multiple Object Tracking},
  doi={10.1109/ICIP.2016.7533003}
}

Credit

This implementation was fully taken from this repository.

This repository simplifies the deployment of the above model by:

• Fixing some bugs when there is no detection in a frame and
• Making deployment cross-compatible with sklearn and scipy's implementation of linear_assignment

Reference

Github@ guillelopez

Github@ yhengchen