Book Image

OpenCV By Example

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

OpenCV By Example

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

Overview of this book

Open CV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you are completely new to the concept of Computer Vision or have a basic understanding of it, this book will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects. Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis or text recognition in complex scenes, and will take you through the commonly used Computer Vision techniques to build your own Open CV projects from scratch. By the end of this book, you will be familiar with the basics of Open CV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition.
Table of Contents (18 chapters)
OpenCV By Example
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Chapter 9. Learning Object Tracking

In the previous chapter, we learned about video surveillance, background modeling, and morphological image processing. We discussed how we can use different morphological operators to apply cool visual effects to input images. In this chapter, we will learn how to track an object in a live video. We will discuss the different characteristics of an object that can be used to track it. We will also learn about different methods and techniques used for object tracking. Object tracking is used extensively in robotics, self-driving cars, vehicle tracking, player tracking in sports, video compression, and so on.

By the end of this chapter, you will learn:

  • How to track colored objects

  • How to build an interactive object tracker

  • What is a corner detector

  • How to detect good features to track

  • How to build an optical flow-based feature tracker