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Learning OpenCV 3 Computer Vision with Python (Update)

Learning OpenCV 3 Computer Vision with Python (Update)

By : Joe Minichino, Joseph Howse
2.1 (7)
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Learning OpenCV 3 Computer Vision with Python (Update)

Learning OpenCV 3 Computer Vision with Python (Update)

2.1 (7)
By: Joe Minichino, Joseph Howse

Overview of this book

OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the development of all sorts of real-world applications, including security and surveillance. Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application.
Table of Contents (11 chapters)
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6
6. Retrieving Images and Searching Using Image Descriptors
10
Index

Detecting moving objects


The first task that needs to be accomplished for us to be able to track anything in a video is to identify those regions of a video frame that correspond to moving objects.

There are many ways to track objects in a video, all of them fulfilling a slightly different purpose. For example, you may want to track anything that moves, in which case differences between frames are going to be of help; you may want to track a hand moving in a video, in which case Meanshift based on the color of the skin is the most appropriate solution; you may want to track a particular object of which you know the aspect, in which case techniques such as template matching will be of help.

Object tracking techniques can get quite complex, let's explore them in the ascending order of difficulty, starting from the simplest technique.

Basic motion detection

The first and most intuitive solution is to calculate the differences between frames, or between a frame considered "background" and all the...

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