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YOLO and computer vision for traffic management.CNN & OpenCV
Published 4/2024
Created by Sunny Kumar
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 12 Lectures ( 1h 53m ) | Size: 1.23 GB[/center]
Revolutionising traffic management with YOLO and DeepSort. Detect and track object using YOLOV8. Region based counting.
What you'll learn:
you will explore various tracking algorithms and methodologies integrated with YOLO to enable robust and reliable tracking of objects across frames.
Understand basics of opencv to combine
You will learn about techniques such as Kalman filtering, and deep association learning for tracking objects through occlusions and cluttered environments.
combine deep sort and yolo for object tracking and counting.
count number of vehicle moving on a highway.
Count number of vehicles moving in and out in a multilane highway environment
Use OpenCV to work with Image and Video Files.
Understand the fundamentals of Object Detection and learn how to use YOLO Algorithm to do Object Detection with YOLOv8
Understand concepts of deepsort and yolov8 to distinctly identify vehicles in cluttered environment.
Estimate the movement of Vehicles through generation of heatmap using yolo
Understand how to generate heatmap using yolo and deepsort
Revolutionise traffic management using object tracking and object counting
Understand how to do video analytics using computer vision algorithm.
Understand how openCV can be used to do frame extraction from video
basics steps of how to apply yolo on image and video
region based object counting
Requirements:
Python basics
| Code: |
| https://www.udemy.com/course/yolo-and-computer-vision-for-traffic-managementcnn-opecv/ |
