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Computer Vision with OpenCV 3 and Qt5

You're reading from   Computer Vision with OpenCV 3 and Qt5 Build visually appealing, multithreaded, cross-platform computer vision applications

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788472395
Length 486 pages
Edition 1st Edition
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Author (1):
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Amin Ahmadi Tazehkandi Amin Ahmadi Tazehkandi
Author Profile Icon Amin Ahmadi Tazehkandi
Amin Ahmadi Tazehkandi
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Table of Contents (14) Chapters Close

Preface 1. Introduction to OpenCV and Qt 2. Creating Our First Qt and OpenCV Project FREE CHAPTER 3. Creating a Comprehensive Qt+OpenCV Project 4. Mat and QImage 5. The Graphics View Framework 6. Image Processing in OpenCV 7. Features and Descriptors 8. Multithreading 9. Video Analysis 10. Debugging and Testing 11. Linking and Deployment 12. Qt Quick Applications 13. Other Books You May Enjoy

Background/foreground detection


Background/foreground detection, or segmentation, which is often also to as background subtraction for quite good reasons, is the method of differentiating between the moving or changing regions in an image (foreground), as opposed to the regions that are more or less constant or static (background). This method is also very effective in detecting motions in an image. OpenCV includes a number of different methods for background subtraction, with two of them being available in the OpenCV installation by default, namely BackgroundSubtractorKNN and BackgroundSubtractorMOG2. Similar to the feature detector classes we learned about in Chapter 7, Features and Descriptors, these classes also originate from the cv::Algorithm class, and they are both used quite easily and similarly since they differ not in the usage or the result, but in the implementation of the classes.

BackgroundSubtractorMOG2 can be used to detect the background/foreground by using the Gaussian...

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