Object Tracking with OpenCV

Computer Vision

Advanced tracking techniques made simple by Python & OpenCV

Engineers and computer scientists have long been trying to allow computers to see and interpret visual data and perform certain functions based on the data collected. That is where the idea of computer vision arose. Computer vision aims to automate the processes of machines, which can be performed by human vision. You can say as artificial intelligence has given computers the ability to think, computer vision gives them the ability to see and interpret sight. Computer vision allows the machines to perform the functions of the human eye and mind, but instead of nerves and retinas, this has to be done with the help of cameras and algorithms.

For the past two decades, AI-driven computer vision has provided multiple methods to perform even a single function that a human brain can accomplish related to vision. The implementation of modern computer vision techniques has exponentially revolutionized technology. Now, it is being adapted in almost every other tech domain, be it medical diagnosis, autonomous cars, or background removal from images and videos. Moving on to the problem of object tracking, the following article probs the concept in a much deeper way. We start from the basics and will move on to the full implementation of an object tracking algorithm using OpenCV. The article is divided into the following sections:

Object tracking is one such application of computer vision where an object is detected in a video, otherwise interpreted as a set of frames, and the object’s trajectory is estimated. For instance, you have a video of a baseball match, and you want to track the ball’s location constantly throughout the video. Object tracking is the method of tracking the ball’s location across the screen in real-time by estimating its trajectory.

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