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OpenCV Essentials

You're reading from   OpenCV Essentials Acquire, process, and analyze visual content to build full-fledged imaging applications using OpenCV

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Product type Paperback
Published in Aug 2014
Publisher
ISBN-13 9781783984244
Length 214 pages
Edition 1st Edition
Tools
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Toc

Table of Contents (10) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Something We Look At – Graphical User Interfaces 3. First Things First – Image Processing 4. What's in the Image? Segmentation 5. Focusing on the Interesting 2D Features 6. Where's Wally? Object Detection 7. What Is He Doing? Motion 8. Advanced Topics Index

The Gunnar-Farneback optical flow


The Gunnar-Farneback algorithm was developed to produce dense Optical Flow technique results (that is, on a dense grid of points). The first step is to approximate each neighborhood of both frames by quadratic polynomials. Afterwards, considering these quadratic polynomials, a new signal is constructed by a global displacement. Finally, this global displacement is calculated by equating the coefficients in the quadratic polynomials' yields.

Let's now see the implementation of this method, which uses the calcOpticalFlowFarneback()function. The following is an example (maxMovementFarneback) that uses this function to detect the maximum movement as shown in the previous example:

void maxMovementFarneback(Mat& prev_frame, Mat& frame)
{
    // 1-Set the Parameters
    Mat optical_flow = Mat(prev_frame.size(), COLOR_BGR2GRAY);
    double pyr_scale = 0.5;
    int levels = 3;
    int win_size = 5;
    int iterations = 5;
    int poly_n = 5;
    double poly_sigma...
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