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I will build realtime accident detection system
About this Gig
CrashVisionAI is an AI-powered computer vision system designed to detect vehicle accidents from traffic and CCTV footage using YOLOv8, OpenCV, and Flask. The system analyzes uploaded videos frame-by-frame, detects vehicles in real time, tracks their movement using BotSort tracking, and identifies possible collisions using custom motion analysis algorithms.
The project combines object detection, vehicle tracking, overlap analysis (IoU), speed estimation, and directional movement analysis to reduce false positives and improve accident detection accuracy. CrashVisionAI can classify accidents into LOW, MEDIUM, and HIGH severity levels while also generating timestamps and confidence scores for each detected crash.
A modern Flask-based web interface allows users to upload traffic footage and instantly receive AI-generated crash analysis reports. The system also supports multiple crash detection within a single video and provides a professional dashboard for displaying results.
This project demonstrates practical applications of deep learning, computer vision, video analytics, and AI-powered automation for intelligent traffic monitoring systems.
Programming language:
Python
•
SQL
•
Java
Tools:
Jupyter Notebook
•
OpenCV
•
TensorFlow
My Portfolio
FAQ
Q1. What does CrashVisionAI do?
CrashVisionAI analyzes traffic or CCTV footage and automatically detects possible vehicle accidents using AI and computer vision techniques.
Q2. Which technologies were used in this project?
The project was built using YOLOv8, OpenCV, Flask, Python, and BotSort tracking.
Q3. Can the system detect multiple accidents in one video?
Yes. CrashVisionAI supports multiple collision detection within a single uploaded video.
Q4. Does the system classify accident severity?
Yes. Detected accidents are classified into LOW, MEDIUM, or HIGH severity levels based on motion analysis.
Q5. What type of videos are supported?
The system works with traffic camera footage, dashcam videos, highway recordings, and CCTV surveillance videos.
Q6. Is this a real-time system?
The current version mainly processes uploaded videos, but the architecture can be extended for real-time CCTV monitoring.
Q7. Does the project include a web interface?
Yes. A Flask-based web dashboard allows users to upload videos and view AI-generated crash reports.
Q8. Which AI model is used for vehicle detection?
YOLOv8 is used for real-time vehicle detection and tracking.
Q9. Can this project be customized?
Yes. The system can be modified for different traffic environments, custom datasets, or advanced analytics features.
Q10. What skills does this project demonstrate?
The project demonstrates skills in computer vision, deep learning, AI model integration, Flask development, OpenCV, and video analytics.
