Inspiration
The inspiration behind VisionDrive AI came from the increasing traffic congestion, road accidents, and the growing need for intelligent traffic monitoring systems in smart cities. Traditional traffic surveillance systems require continuous human monitoring, which is time-consuming and less efficient. This project was developed to explore how Artificial Intelligence and Computer Vision can automate traffic analysis and improve road safety through real-time vehicle detection and monitoring.
What it does
VisionDrive AI is an intelligent traffic surveillance system that analyzes CCTV traffic footage using Deep Learning and Computer Vision techniques. The system uses the YOLOv8 object detection model to detect and classify vehicles such as cars, buses, bikes, trucks, and pedestrians from traffic videos.
The project processes traffic footage frame by frame, identifies objects in real time, and generates annotated output videos with bounding boxes and labels around detected vehicles. The system can also be extended in the future for accident detection, helmet detection, vehicle counting, and automatic number plate recognition.
How we built it
The project was developed using Python as the primary programming language. OpenCV was used for video processing and frame extraction, while the YOLOv8 deep learning model from Ultralytics was integrated for object detection and classification.
The workflow of the system includes:
Collecting traffic CCTV videos Extracting video frames Preprocessing frames Applying YOLOv8 for object detection Generating annotated output videos
The project structure was organized into separate folders for datasets, detection modules, outputs, and models to maintain scalability and modularity.
Challenges we ran into
Several challenges were encountered during the development process:
Handling corrupted or unsupported traffic video files Configuring Python libraries and dependencies correctly Managing video codec compatibility issues Improving detection performance for different traffic conditions Organizing project structure and file management
These challenges were solved by using compatible video formats, optimized OpenCV configurations, pretrained YOLOv8 models, and structured project organization.
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