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Learn OpenCV 4 by Building Projects

Learn OpenCV 4 by Building Projects

By : Millán Escrivá, Vinícius G. Mendonça, Joshi
2.5 (2)
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Learn OpenCV 4 by Building Projects

Learn OpenCV 4 by Building Projects

2.5 (2)
By: Millán Escrivá, Vinícius G. Mendonça, 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)
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Video Surveillance, Background Modeling, and Morphological Operations

In this chapter, we are going to learn how to detect a moving object in a video taken from a static camera. This is used extensively in video surveillance systems. We will discuss the different characteristics that can be used to build this system. We will learn about background modeling and see how we can use it to build a model of the background in a live video. Once we do this, we will combine all the blocks to detect the object of interest in the video.

By the end of this chapter, you should be able to answer the following questions:

  • What is naive background subtraction?
  • What is frame differencing?
  • How do we build a background model?
  • How do we identify a new object in a static video?
  • What is morphological image processing and how is it related to background modeling?
  • How do we achieve different effects...

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