This project imlements the following tasks in the project: 1. Vehicle counting, 2. Lane detection. 3.Lane change detection and 4.speed estimation

This computer vision project analyzes tennis match videos using cutting-edge techniques. It employs YOLOv8 for player detection, finetuned YOLO for ball tracking, and ResNet50 for extracting court keypoints. Additionally, it calculates player and ball speeds and generates a mini court reflecting player positions.

This project leverages YOLO (You Only Look Once) to detect various traffic violations in real-time, aimed at improving road safety and compliance.

Detect speed of vehicles on multi-lane highways.

License Plate Recognition and Speed detection using a camera.

Speed Estimate

Monitor the speed of people driving in front of my house with a raspberry pi camera, zero2 w, and the speed-camera repo I cloned from Pageauc. 🚗 🚓

A script to track objects and estimate their speed in videos

A computer vision toolkit for traffic analysis with vehicle detection, speed estimation, segmentation, and number plate recognition.

Mobile object speed detection app that utilizes Machine Learning and Computer Vision.

Real-time object detection and tracking using YOLO11 with SRT (Secure Reliable Transport) video streams. Tracks vehicles, people, and bicycles with direction detection, speed calculation, and automatic screenshot capture. Includes comprehensive time-series metrics storage with InfluxDB and Grafana visualization.

Detects car speed from mounted dash cam

Vehicle speed detection - license plate recognition - upload data to database - send message to vehicle owner

Real-Time Vehicle Speed Detection and Counting System

Helmet-Numberplate-Speed Detection is an advanced system designed to detect helmets, recognize vehicle number plates, and measure speed, ensuring traffic safety and law enforcement compliance.

CornerSentinal - Advanced Early Warning System (Capstone Project)

🖼️ Explore modular computer vision projects that apply OOP design for clear functionalities and effective error handling, enhancing image processing tasks.