To track anything in a video, first, we must identify the regions of a video frame that correspond to moving objects. Many motion detection techniques are based on the simple concept of background subtraction. For example, suppose that we have a stationary camera viewing a scene that is also mostly stationary. In addition to this, suppose that the camera's exposure and the lighting conditions in the scene are stable so that frames do not vary much in terms of brightness. Under these conditions, we can easily capture a reference image that represents the background or, in other words, the stationary components of the scene. Then, any time the camera captures a new frame, we can subtract the frame from the reference image, and take the absolute value of this difference in order to obtain a measurement of motion at each pixel...
Learning OpenCV 4 Computer Vision with Python 3
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Learning OpenCV 4 Computer Vision with Python 3
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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)
Preface
Setting Up OpenCV
Free Chapter
Handling Files, Cameras, and GUIs
Processing Images with OpenCV
Depth Estimation and Segmentation
Detecting and Recognizing Faces
Retrieving Images and Searching Using Image Descriptors
Building Custom Object Detectors
Tracking Objects
Camera Models and Augmented Reality
Introduction to Neural Networks with OpenCV
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Appendix A: Bending Color Space with the Curves Filter
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