Multiple Object Tracker, Based on Hungarian algorithm + Kalman filter.

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

Vehicle Detection Using Deep Learning and YOLO Algorithm

The easiest way to count pedestrians, cyclists, and vehicles on edge computing devices or live video feeds.

This project utilizes PyQt5, YOLOv8, and TensorFlow for vehicle detection and identification, including speed monitoring and fine issuance. It features a user-friendly interface and contributes to traffic discipline through automated enforcement.

Vehicle counting in a conjusted traffic road where background subtraction gives lower performance.

A car-counting system using background subtraction on a video feed. It makes use of OpenCV API.

This app detects types of cars and counts cars using YOLOv3

Multi-lane Car Detection & Counting using Python OpenCV

In this frame, the YOLOv8 Nano model detects and labels multiple people (highlighted in green) and vehicles (highlighted in red) from a movie scene. The detection overlay displays

Here is a source code for car counting using YOLOv8n model. It is a basic example of computer vision and object detection task.

Detecting and counting cars on the video