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OpenCV By Example

You're reading from   OpenCV By Example Enhance your understanding of Computer Vision and image processing by developing real-world projects in OpenCV 3

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Product type Paperback
Published in Jan 2016
Publisher Packt
ISBN-13 9781785280948
Length 296 pages
Edition 1st Edition
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Authors (3):
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Vinícius G. Mendonça Vinícius G. Mendonça
Author Profile Icon Vinícius G. Mendonça
Vinícius G. Mendonça
David Millán Escrivá David Millán Escrivá
Author Profile Icon David Millán Escrivá
David Millán Escrivá
Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with OpenCV 2. An Introduction to the Basics of OpenCV FREE CHAPTER 3. Learning the Graphical User Interface and Basic Filtering 4. Delving into Histograms and Filters 5. Automated Optical Inspection, Object Segmentation, and Detection 6. Learning Object Classification 7. Detecting Face Parts and Overlaying Masks 8. Video Surveillance, Background Modeling, and Morphological Operations 9. Learning Object Tracking 10. Developing Segmentation Algorithms for Text Recognition 11. Text Recognition with Tesseract 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...

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