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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

Feature based tracking


Feature based tracking refers to tracking individual feature points across successive frames in the video. We use a technique called optical flow to track these features. Optical flow is one of the most popular techniques in computer vision. We choose a bunch of feature points and track them through the video stream.

When we detect the feature points, we compute the displacement vectors and show the motion of those keypoints between consecutive frames. These vectors are called motion vectors. There are many ways to do this, but the Lucas-Kanade method is perhaps the most popular of all these techniques. You can refer to their original paper at http://cseweb.ucsd.edu/classes/sp02/cse252/lucaskanade81.pdf. We start the process by extracting the feature points. For each feature point, we create 3x3 patches with the feature point in the center. The assumption here is that all the points within each patch will have a similar motion. We can adjust the size of this window...

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