نظام ذكي لاكتشاف وتتبع الركشات

Rickshaw Detection and Tracking System

A real-time AI-powered system designed to detect and track rickshaws in video streams using a custom-trained YOLO12 model. The system assigns unique IDs to each detected rickshaw and displays clear visual annotations for traffic monitoring and urban mobility analysis.

Project Overview

RickshawVision AI is a specialized object detection and tracking solution focused on rickshaws in urban environments. By leveraging a custom YOLO12 model, the system accurately detects multiple rickshaws, tracks them across frames, and visualizes their movement using bounding boxes, labels, and persistent tracking IDs. The project is ideal for smart traffic systems and urban mobility analytics.

Key Features

Custom YOLO12 Rickshaw Detection

• 🛺 Detects rickshaws using a model trained specifically on rickshaw datasets.
• ⚡ High accuracy in complex urban traffic scenes.

Object Tracking with Unique IDs

• 🆔 Assigns persistent tracking IDs to each rickshaw.
• 🔁 Tracks rickshaw movement across multiple video frames.

Real-Time Video Processing

• 🎥 Supports live camera feeds and recorded video files.
• ⏩ Frame skipping for optimized real-time performance.

Visual Annotations

• 📦 Bounding boxes with rickshaw labels.
• 🏷️ Clearly displayed tracking IDs on each detected vehicle.

Mouse Coordinate Debugging

• 🖱️ Displays real-time mouse (x, y) coordinates.
• 🛠️ Useful for debugging, calibration, and annotation tasks.

Technology Stack

• 🐍 Programming Language: Python
• 🎥 Computer Vision: OpenCV
• 🧠 Object Detection & Tracking: Ultralytics YOLO12
• 🖥️ Visualization & Drawing: cvzone

Use Cases

• 🚦 Traffic Management – Monitor rickshaw flow and density.
• 🏙️ Urban Mobility Analytics – Analyze rickshaw movement patterns.
• 🛡️ Safety & Compliance – Support enforcement of traffic rules.
• 📹 Smart City Surveillance – Integrate into intelligent monitoring systems.

Conclusion

This Rickshaw Detection and Tracking system highlights the effectiveness of custom-trained YOLO12 models for specialized real-time detection tasks. By accurately detecting and tracking rickshaws with unique IDs and clear visual annotations, the system provides valuable insights for traffic management, urban planning, and smart city initiatives. The solution is scalable and can be extended to additional vehicle types or deployed on edge devices for on-site monitoring.