This project involves developing a Traffic Flow Analyzer, a deep learning-based tool designed for real-time traffic monitoring and analysis. Built during Hacktoberfest 2024 as an open-source initiative, the analyzer leverages the YOLOv11x object detection model and the BoT-SORT tracker to detect and track vehicles. It estimates pixel-based speed by calculating the Euclidean distance between successive frames, providing valuable insights into traffic conditions, vehicle counts, and congestion hotspots. This tool is tailored for urban planners, transport authorities, and smart city initiatives, aiming to enhance traffic management and road safety. 

Model Used: YOLOv11x for object detection and BoT-SORT for vehicle tracking. 

Speed Estimation: Pixel-based speed estimation is performed using Euclidean distance calculations to track vehicle movement frame by frame. 

Key Insights Provided:- 
-> Total vehicles count and breakdown by vehicle class (e.g., cars, trucks, bikes).
-> Identification of congestion hotspots based on vehicle density and average speed. 
-> Real-time congestion status reports.