YOLOv3 + Deep_SORT Vehicle Speed Estimation Tracker

• demo Video: https://youtu.be/Dn9S8p49dqE

team member

🏃 Choi sanghyun 🏃 Kim sungjo 🏃 Park meenhee

dependencies

• OpenCV
• sklean
• pillow
• numpy 1.15.0
• tensorflow-gpu 1.13.1
• CUDA 10.0

---

It uses:

• Detection: YOLOv3

• Tracking: Deep_SORT

• ->:object detection and tracking (python)

Quick Start

0.Requirements

pip install -r requirements.txt

1. Download the code to your computer.

git clone https://github.com/yehengchen/Object-Detection-and-Tracking.git

2. Download [yolov3.weights] and place it in deep_sort_yolov3/model_data/

3. Convert the Darknet YOLO model to a Keras model:

$ python convert.py model_data/yolov3.cfg model_data/yolov3.weights model_data/yolo.h5

4. Run the YOLO_DEEP_SORT:

$ python main.py -c [CLASS NAME] -i [INPUT VIDEO PATH]

$ python main.py -c person -i ./test_video/testvideo.avi

Citation

YOLOv3 :

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

Deep_SORT :

@inproceedings{Wojke2017simple,
title={Simple Online and Realtime Tracking with a Deep Association Metric},
author={Wojke, Nicolai and Bewley, Alex and Paulus, Dietrich},
booktitle={2017 IEEE International Conference on Image Processing (ICIP)},
year={2017},
pages={3645--3649},
organization={IEEE},
doi={10.1109/ICIP.2017.8296962}
}

@inproceedings{Wojke2018deep,
title={Deep Cosine Metric Learning for Person Re-identification},
author={Wojke, Nicolai and Bewley, Alex},
booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
year={2018},
pages={748--756},
organization={IEEE},
doi={10.1109/WACV.2018.00087}
}

Reference

Github:deep_sort@Nicolai Wojke nwojke

Github:deep_sort_yolov3@Qidian213

Github:Object-Detection-and-Tracking@yehengchen