A real-time AI-powered Collision Alert System designed to enhance vehicle safety using Computer Vision and Deep Learning techniques. The system uses YOLOv8 object detection, object tracking, trajectory analysis, and distance estimation to detect potential collision risks in dynamic traffic environments.
- Real-time object detection using YOLOv8
- Collision prediction and alert generation
- Object tracking and trajectory analysis
- Distance estimation using computer vision techniques
- Dynamic ROI (Region of Interest) processing
- Angular analysis for collision risk detection
- Real-time performance optimization using OpenCV and PyTorch
- Vehicle and pedestrian detection support
- Python
- YOLOv8
- OpenCV
- PyTorch
- NumPy
- Computer Vision
- Deep Learning
- Capture real-time video input
- Detect objects using YOLOv8
- Track detected objects across frames
- Estimate distance and trajectory
- Analyze collision probability
- Generate collision alerts in real-time
- Real-time collision detection and monitoring
- High detection accuracy with optimized performance
- Intelligent object tracking and movement prediction
- Designed for dynamic traffic environments
- Supports proactive vehicle safety systems
- Multi-camera integration
- AI-based driving assistance
- Autonomous braking support
- Cloud-based analytics
- Advanced lane detection integration
# Clone the repository
git clone https://github.com/your-username/collision-alert-system.git
# Navigate to project directory
cd collision-alert-system
# Install dependencies
pip install -r requirements.txt
# Run the project
python app.pyCollision-Alert-System/
│
├── models/
├── dataset/
├── outputs/
├── utils/
├── app.py
├── requirements.txt
└── README.md- Bijjam Rajendra Reddy
- Team Members – Kalasalingam University
This project was developed as part of a research-oriented computer vision system for vehicle safety and collision prediction using AI-based techniques.
Give this repository a star ⭐ and support the project.