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.
Real-time Traffic and Pedestrian Counting (YOLOV3 in tensorflow2)
Vehicle counting in a conjusted traffic road where background subtraction gives lower performance.
实时车辆行人交通流计数Real-time Vehicle and Pedestrian Counting (CenterNet)
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
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
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