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

Understanding background subtraction


Background subtraction is very useful in video surveillance. Basically, the background subtraction technique performs really well in cases where we need to detect moving objects in a static scene. Now, how is this useful for video surveillance? The process of video surveillance involves dealing with a constant data flow. The data stream keeps coming in at all times, and we need to analyze it to identify any suspicious activities. Let's consider the example of a hotel lobby. All the walls and furniture have a fixed location. Now, if we build a background model, we can use it to identify suspicious activities in the lobby. We can take advantage of the fact that the background scene remains static (which happens to be true in this case). This helps us avoid any unnecessary computation overheads.

As the name suggests, this algorithm works by detecting the background and assigning each pixel of an image to two classes: either the background (assuming that it...