Real-Time Construction Equipment Tracking & Activity Analysis System

A real-time AI-powered construction equipment monitoring system designed to track heavy machinery, analyze movement patterns, and generate operational insights for construction sites.

The system utilizes YOLOv11 for object detection and BoT-SORT for multi-object tracking, enabling accurate tracking of construction equipment across video streams.

Motion analysis is performed using Optical Flow techniques to measure equipment activity and movement behavior. A Kafka streaming pipeline was implemented to process data in real time, while FastAPI provides backend services for analytics and reporting.

All tracking and activity data are stored in TimescaleDB and visualized through an interactive Streamlit dashboard that provides operational metrics and equipment performance insights.

Key Features:

• Real-time equipment detection and tracking.

• Multi-object tracking using BoT-SORT.

• Optical Flow-based activity analysis.

• Kafka streaming pipeline.

• FastAPI backend services.

• TimescaleDB data storage.

• Interactive analytics dashboard.

• Operational performance monitoring.

Technologies Used:

Python, YOLOv11, OpenCV, BoT-SORT, FastAPI, Kafka, TimescaleDB, Streamlit, Computer Vision.