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Learning OpenCV 4 Computer Vision with Python 3

Learning OpenCV 4 Computer Vision with Python 3

By : Joseph Howse, Joe Minichino
4.1 (14)
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Learning OpenCV 4 Computer Vision with Python 3

Learning OpenCV 4 Computer Vision with Python 3

4.1 (14)
By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You’ll be able to put theory into practice by building apps with OpenCV 4 and Python 3. You’ll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you’ll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you’ll have opportunities for hands-on activities. Next, you’ll tackle two popular challenges: face detection and face recognition. You’ll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you’ll develop your skills in 3D tracking and augmented reality. Finally, you’ll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age. By the end of this book, you’ll have the skills you need to execute real-world computer vision projects.
Table of Contents (13 chapters)
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Tracking Objects

In this chapter, we will explore a selection of techniques from the vast topic of object tracking, which is the process of locating a moving object in a movie or a video feed from a camera. Real-time object tracking is a critical task in many computer vision applications such as surveillance, perceptual user interfaces, augmented reality, object-based video compression, and driver assistance.

Tracking objects can be accomplished in several ways, with the most optimal technique being largely dependent on the task at hand. We will take the following route in our study of this topic:

  • Detect moving objects based on differences between the current frame and a frame that represents the background. First, we will try a simple implementation of this approach. Then, we will use OpenCV's implementations of more advanced algorithms, namely, the Mixture of Gaussians...
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