Introduction
YOLOvX enhances road safety by detecting pedestrians, monitoring traffic violations, and assisting autonomous vehicles.
Problem Statement
Security teams rely on manual monitoring of CCTV footage, making it difficult to detect suspicious activities, unauthorized access, or security threats in real time.
Application
- Pedestrian & Vehicle Detection: Prevents accidents by identifying road users.
- Traffic Violation Monitoring: Detects red-light violations and overspeed.
- License Plate Recognition: Automates toll collection and law enforcement surveillance.
Usecase
Self-Driving Car Technology
Implementation
A robust vehicle detection algorithm using Support Vector Machines (SVM) for autonomous vehicles with key features:
- SVM trained with Histogram of Gradient (HOG) and other advanced features
- Distinguishes between cars and non-car objects with high accuracy
- Incorporates sliding window-based lane detection for enhanced awareness
- While deep learning methods like YOLO and SSD have gained popularity, this SVM approach shows that traditional machine learning still has a place in modern autonomous systems.
Traffic Analysis with AI Technology
Implementation
Leveraging the YOLO models for real-time vehicle detection and tracking, offering precise insights into traffic movement.
Key Highlights:
- Accurate vehicle counting (top-to-bottom/bottom-to-top).
- AI-powered detection + OpenCV tracking.
- Visualized reports in real-time video format.
Benefits
- Reduces road accidents through smart AI alerts.
- Enhances traffic management efficiency.
- Supports autonomous vehicle navigation with real-time object detection.
