Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By : David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot
Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By: David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot

Overview of this book

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books: •Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá •Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi
Table of Contents (28 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Building an interactive object tracker


A colorspace-based tracker gives us the freedom to track a colored object, but we are also constrained to a predefined color. What if we just want to pick an object at random? How do we build an object tracker that can learn the characteristics of the selected object and just track it automatically? This is where the continuously-adaptive meanshift (CAMShift) algorithm comes into picture. It's basically an improved version of the meanshift algorithm.

The concept of meanshift is actually nice and simple. Let's say we select a region of interest and we want our object tracker to track that object. In this region, we select a bunch of points based on the color histogram and we compute the centroid of spatial points. If the centroid lies at the center of this region, we know that the object hasn't moved. But if the centroid is not at the center of this region, then we know that the object is moving in some direction. The movement of the centroid controls...