Book Image

Learn OpenCV 4 By Building Projects - Second Edition

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

Learn OpenCV 4 By Building Projects - Second Edition

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

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. Whether you’re completely new to computer vision, or have a basic understanding of its concepts, Learn OpenCV 4 by Building Projects – Second edition will be your guide to understanding OpenCV concepts and algorithms through real-world examples and projects. You’ll begin with the installation of OpenCV and the basics of image processing. Then, you’ll cover user interfaces and get deeper into image processing. As you progress through the book, you'll learn complex computer vision algorithms and explore machine learning and face detection. The book then guides you in creating optical flow video analysis and background subtraction in complex scenes. In the concluding chapters, you'll also learn about text segmentation and recognition and understand the basics of the new and improved deep learning module. By the end of this book, you'll be familiar with the basics of Open CV, such as matrix operations, filters, and histograms, and you'll have mastered commonly used computer vision techniques to build OpenCV projects from scratch.
Table of Contents (14 chapters)

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 are going to 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 for object tracking. Object tracking is used extensively in robotics, self-driving cars, vehicle tracking, player tracking in sports, and video compression.

By the end of this chapter, you will know the following:

  • How to track objects of a specific color
  • How to build an interactive object tracker
  • What a corner detector is
  • How to detect good features to track
  • How to build an optical flow-based feature tracker...