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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

Building an interactive object tracker


Colorspace based tracker gives us the freedom to track a colored object, but we are also constrained to a predefined color. What if we just want to pick an object at random? How do we build an object tracker that can learn the characteristics of the selected object and just track it automatically? This is where the CAMShift algorithm, which stands for Continuously Adaptive Meanshift, comes into the picture. It's basically an improved version of the Meanshift algorithm.

The concept of Meanshift is actually nice and simple. Let's say we select a region of interest and we want our object tracker to track that object. In that region, we select a bunch of points based on the color histogram and compute the centroid. If the centroid lies at the center of this region, we know that the object hasn't moved. But if the centroid is not at the center of this region, then we know that the object is moving in some direction. The movement of the centroid controls the...

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