Video Object Counting: A Step-by-Step Guide
Blog post from Roboflow
In a comprehensive guide on using computer vision for object counting in videos, tools like OpenCV, Roboflow Supervision, and YOLOv8 are highlighted as essential components. OpenCV serves as the interface for processing video feeds and extracting frames for object detection. Roboflow Inference facilitates the use of pre-trained models for identifying objects, while Supervision aids in annotating predictions and managing tracking. The guide demonstrates a method for setting up a system that captures video input, preprocesses frames using OpenCV, detects objects with YOLOv8, and tracks them across frames with ByteTrack, focusing on counting objects in defined polygon zones. The process involves defining a target zone through user-drawn polygons and updating object counts as they enter or exit these zones, with the results visually annotated on video frames. The guide also provides code snippets and detailed steps for implementing the system, emphasizing its application in various scenarios like sports analytics and assembly line monitoring.