Object Tracking Using OpenCV
Object Tracking involves analyzing individual frames of a video, comparing them to previous frames to detect and monitor objects. Initially, objects are identified, assigned unique IDs if multiple, and their trajectories are monitored and updated in subsequent frames. OpenCV offers built-in and external tracker libraries like GOTURN, MIL, Nano, Vit, mean shift, and camshift, each with varying speed and accuracy. Here, we’ll focus on implementing mean shift, known for its ease of use, for object tracking.
Getting Started With Object Tracking Using OpenCV
OpenCV, developed by Intel in the early 2000s, is a popular open-source computer vision library used for real-time tasks. It offers various features like image processing, face detection, object detection, and more. In this article, we explore object-tracking algorithms and how to implement them using OpenCV and Python to track objects in videos.
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