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OpenCV with Python By Example

You're reading from   OpenCV with Python By Example Build real-world computer vision applications and develop cool demos using OpenCV for Python

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Product type Paperback
Published in Sep 2015
Publisher Packt
ISBN-13 9781785283932
Length 296 pages
Edition 1st Edition
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Author (1):
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Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Toc

Table of Contents (14) Chapters Close

Preface 1. Applying Geometric Transformations to Images FREE CHAPTER 2. Detecting Edges and Applying Image Filters 3. Cartoonizing an Image 4. Detecting and Tracking Different Body Parts 5. Extracting Features from an Image 6. Creating a Panoramic Image 7. Seam Carving 8. Detecting Shapes and Segmenting an Image 9. Object Tracking 10. Object Recognition 11. Stereo Vision and 3D Reconstruction 12. Augmented Reality Index

Background subtraction

Background subtraction is very useful in video surveillance. Basically, background subtraction technique performs really well for cases where we have to detect moving objects in a static scene. As the name indicates, this algorithm works by detecting the background and subtracting it from the current frame to obtain the foreground, that is, moving objects. In order to detect moving objects, we need to build a model of the background first. This is not the same as frame differencing because we are actually modeling the background and using this model to detect moving objects. So, this performs much better than the simple frame differencing technique. This technique tries to detect static parts in the scene and then include it in the background model. So, it's an adaptive technique that can adjust according to the scene.

Let's consider the following image:

Background subtraction

Now, as we gather more frames in this scene, every part of the image will gradually become a part of the...

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