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https://github.com/raj-tyagi/smart-surveillance-system
Smart Surveillance System combines YOLOv4 detection and optical flow tracking to monitor objects in video streams, supporting local files and YouTube links with real-time visualization.
https://github.com/raj-tyagi/smart-surveillance-system
computer-vision deep-learning machine-learning-automation object-detection opencv optical-flow python smart-surveillance tracking-security-monitoring video-processing yolov4
Last synced: 16 days ago
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Smart Surveillance System combines YOLOv4 detection and optical flow tracking to monitor objects in video streams, supporting local files and YouTube links with real-time visualization.
- Host: GitHub
- URL: https://github.com/raj-tyagi/smart-surveillance-system
- Owner: raj-tyagi
- License: mit
- Created: 2024-12-29T18:57:18.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-12-29T19:19:57.000Z (about 1 month ago)
- Last Synced: 2024-12-29T20:17:51.505Z (about 1 month ago)
- Topics: computer-vision, deep-learning, machine-learning-automation, object-detection, opencv, optical-flow, python, smart-surveillance, tracking-security-monitoring, video-processing, yolov4
- Language: Python
- Homepage:
- Size: 16.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Smart Surveillance System
A robust video processing application that combines **YOLO object detection** with **Lucas-Kanade optical flow-based tracking** to monitor and analyze video streams for smart surveillance.
---
## Features
- **Object Detection**: Utilizes YOLOv4 for real-time detection of objects in video frames.
- **Object Tracking**: Tracks detected objects using Lucas-Kanade Optical Flow, ensuring smooth monitoring across frames.
- **Input Flexibility**: Supports video files and YouTube URLs for seamless integration.
- **Output Video**: Saves processed video with tracked objects as `output_video.avi`.
- **Interactive Display**: Visualizes object detection and tracking in each frame during processing.
---
## Getting Started
### Prerequisites
Ensure the following libraries are installed:
- `opencv-python`
- `numpy`
- `pytube` (for handling YouTube videos)
Install dependencies using pip:
```bash
pip install opencv-python numpy pytube
```
### Files Required
1. **YOLO Files**:
- `yolov4.weights` (pre-trained weights)
- `yolov4.cfg` (YOLO configuration file)
- `coco.names` (class labels from the COCO dataset)
---
## Usage
1. Clone the repository:
```bash
git clone https://github.com/your-username/smart-surveillance.git
cd smart-surveillance
```
2. Run the script:
```bash
python smart_surveillance.py
```
3. Choose input mode:
- **Option 1**: Upload a video from your local device.
- **Option 2**: Provide a YouTube video URL.
---
## How It Works
1. **Detection Phase**:
- Detects objects in video frames using YOLOv4.
- Applies Non-Maximum Suppression (NMS) to eliminate redundant detections.
2. **Tracking Phase**:
- Tracks detected objects across frames using Lucas-Kanade Optical Flow.
- Updates object positions dynamically and removes failed trackers.
3. **Visualization**:
- Draws bounding boxes, labels, and key points for detected and tracked objects.
4. **Output**:
- Saves the processed video as `output_video.avi`.
---
### Example Input and Output Videos
#### Input Video
[Click to view input video](https://drive.google.com/file/d/17vTtRMgAsXipdB2jxjRcHFYVhIHr5pYa/view?usp=sharing)
#### Output Video
[Click to view output video](https://drive.google.com/file/d/1fraAjRt2pFT4yWGjf5X55UhJihzfb68Y/view?usp=sharing)
## Project Structure
```plaintext
|-- smart_surveillance.py # Main script
|-- yolov4.weights # YOLOv4 weights
|-- yolov4.cfg # YOLOv4 configuration file
|-- coco.names # COCO class labels
```
---
## Future Enhancements
- Support for **real-time processing** via live camera feeds.
- Integration of advanced object tracking algorithms (e.g., DeepSORT).
- Transition to **YOLOv8** for improved detection speed and accuracy.
- User-friendly GUI for better interaction.
---
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
---
## Acknowledgments
- [YOLO](https://pjreddie.com/darknet/yolo/) for the object detection framework.
- OpenCV for computer vision tools and functions.
- [PyTube](https://pytube.io/en/latest/) for handling YouTube video downloads.
Feel free to contribute by submitting pull requests or issues! 🎉