Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By : David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot
Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By: David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot

Overview of this book

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books: •Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá •Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi
Table of Contents (28 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Feature-based tracking


Feature-based tracking refers to tracking individual feature points across successive frames in the video. The advantage here is that we don't have to detect feature points in every single frame. We can just detect them once and keep tracking them after that. This is more efficient than running the detector on every frame. 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. A motion vector for a particular point is basically just a directional line indicating where that point has moved, as compared to the previous frame. Different methods are used to detect these motion vectors. The two most popular algorithms are the Lucas-Kanade method...