https://github.com/emiliogrv/object-tracking-core
⚡️Using AI models for Object Detection 👀 and Object Tracking 🚜
https://github.com/emiliogrv/object-tracking-core
image-processing object-detection object-tracking opencv-python video-processing yolov4
Last synced: 10 months ago
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⚡️Using AI models for Object Detection 👀 and Object Tracking 🚜
- Host: GitHub
- URL: https://github.com/emiliogrv/object-tracking-core
- Owner: emiliogrv
- License: gpl-3.0
- Created: 2022-01-25T18:01:29.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-01-28T02:30:35.000Z (over 4 years ago)
- Last Synced: 2025-03-01T17:14:08.519Z (over 1 year ago)
- Topics: image-processing, object-detection, object-tracking, opencv-python, video-processing, yolov4
- Language: Python
- Homepage:
- Size: 9.76 MB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ❓ About this project
This project is made in order to learn and put into practice my knowledge of Python and some AI models implementation, it was born as a personal project after asking myself how many cars pass by my window every day.
I implemented Object detection using YOLOv4 and object tracking using DeepSort and TensorFlow.
It'll work fine with images and videos files and URLs as well as videos streaming platforms like YouTube.
https://user-images.githubusercontent.com/13983577/151476568-679e21ff-83c2-49f8-887d-fcb0a7cad97e.mp4
# 💡 Getting Started
To see how this project works you have two options
### 🟡 Option 1: Online
[](https://colab.research.google.com/github/emiliogrv/object-tracking-core/blob/main/opencv_yolo_deep_sort.ipynb)
### 🟠 Option 2: In local?
1. Clone the repository.
2. Download [YOLO weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights) into **weights** folder.
3. Copy `.env.example` to `.env`.
4. Run `docker-compose build`.
5. Run `docker-compose up`.
6. Open Postman or similar and make a POST to `http://127.0.0.1:5000/track-it`
7. Payload:
```json
{
"output_filename": "some_filename_here",
"source": "your image or video URL here"
}
```
8. Wait until terminal "processed" message.
9. Open **outputs** folder and see the result.
NOTE: By default, this will run only with CPU support.
# 🧰 References
1. [DeepSort YOLOv4 based object tracking](https://github.com/MatPiech/DeepSORT-YOLOv4-TensorRT-OpenVINO)
2. [yolov4-deepsort](https://github.com/theAIGuysCode/yolov4-deepsort)
3. [YOLOv4-Tutorials](https://github.com/augmentedstartups/YOLOv4-Tutorials/blob/master/3.%20YOLOv4%20Video%20and%20Webcam/darknet_video_mod.py#L21)
4. [Deep SORT](https://github.com/mk-michal/deep_sort)
# ❗ Troubleshooting
If you find any problem in my code or anything else, feel free to contact me, open an issue or do a pull request, that
way I can keep learning, and I can improve the code so that way anyone else can learn from it in the best way.