Getting Started With Object Tracking Using OpenCV

Getting Started With Object Tracking Using OpenCV

Below are the concepts that we are going to cover for Getting Started With Object Tracking Using OpenCV in Python:...

What is Object Tracking?

Object tracking in computer vision is following and keeping a record of the position of any object upon its change in movement in the video. Multiple objects can also be tracked simultaneously. The main goal of object tracking is to maintain the identity and positioning of the object as it moves through the video, despite variations in appearance, size, orientation, and lighting, and releasing the object tracking once the object goes out of the video frame....

Applications of Object Tracking

Below are the applications of object tracking using OpenCV:...

Object Detection vs Object Tracking

Object detection and object tracking are distinct processes....

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....

Steps of Object Tracking with OpenCV

Below, are the steps of Object Tracking with OpenCV....

Object Tracking Using OpenCV

Below, are the code of Object Tracking Using OpenCV:...