Speed Guard

Introduction

Road safety is a critical concern globally, with speeding being a major factor behind many accidents. SpeedGuard is an AI-powered video analytics tool that monitors traffic in real-time, detects moving vehicles, estimates their speed based on displacement between video frames, and flags vehicles exceeding a predefined speed threshold.

Problem Statement

Despite the growing number of traffic monitoring systems, many traditional setups rely heavily on expensive hardware (like radar guns or lidar sensors) and lack real-time adaptive intelligence. There is a need for a cost-effective, software-driven solution that can detect speeding vehicles using just video feeds, reducing infrastructure costs and enabling widespread adoption.

Objectives

  • Build a system that can detect and track moving vehicles in video footage.
  • Estimate the relative speed of each vehicle based on motion between frames.
  • Identify and flag vehicles that exceed a user-defined speed threshold.
  • Visually distinguish speeding vehicles for easy monitoring (e.g., red bounding boxes).
  • Provide a modular foundation for potential extensions like real-world speed calibration, alert generation, or integration with traffic management systems.

Product/Project Analysis

  • Detection Mechanism: Utilizes background subtraction (Gaussian Mixture Model) to detect moving objects.
  • Tracking Mechanism: Matches centroids between frames to estimate displacement (proxy for speed).
  • Classification: Vehicles are classified into "Normal" and "Speeding" based on movement thresholds.
  • Visualization: Annotates the video feed with bounding boxes and speed status.
  • Advantages:
    1. Software-only solution requiring no specialized speed sensors.
    2. Lightweight, efficient, and adaptable to different environments.
    3. Scalable for city-wide deployments using edge devices or cloud setups.

Architecture

🎥 Input Video Stream
↓
🧠 Foreground Detection
(Gaussian Mixture Model)
↓
🧹 Noise Removal
(Morphological Opening)
↓
📦 Object Detection
(Blob Analysis - Bounding Boxes + Centroids)
↓
🎯 Centroid Tracking
(Frame-to-Frame)
↓
📏 Speed Estimation
(Displacement Calculation)
↓
⚖️ Speed Comparison
(Against Threshold)
↓
🖍️ Annotation & Display
(Green: Normal | Red: Speeding)

Impact

  • Affordability: No expensive radar or specialized sensors required — only video cameras.
  • Scalability: Can be deployed on existing city surveillance networks without major hardware upgrades.
  • Real-Time Intelligence: Helps authorities react faster by identifying high-risk drivers instantly.
  • Safety Enhancement: Enables proactive management of traffic violations, reducing accidents and fatalities.
  • Data-Driven Traffic Management: The system can be extended to record statistics about traffic behavior, aiding urban planning and law enforcement.

Conclusion

SpeedGuard represents a significant step towards making road safety smarter, more accessible, and technology-driven. By leveraging computer vision techniques, it offers a lightweight, scalable alternative to traditional speed detection systems and lays the foundation for future intelligent traffic management solutions.

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