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

Chapter 8. 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 using morphological operators?