Book Image

Practical Computer Vision

By : Abhinav Dadhich
Book Image

Practical Computer Vision

By: Abhinav Dadhich

Overview of this book

In this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you'll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you'll use them to find similar-looking objects. With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow. You'll get started with semantic segmentation using FCN models and track objects with Deep SORT. Not only this, you will also use Visual SLAM techniques such as ORB-SLAM on a standard dataset. By the end of this book, you will have a firm understanding of the different computer vision techniques and how to apply them in your applications.
Table of Contents (12 chapters)

Tracking

Tracking is the problem of estimating the position of an object over consecutive image sequences. This is also further divided into single object tracking and multiple object tracking, however, both single and multi-object tracking require slightly different approaches. In this section, we will see the methods for multi-object tracking, as well as single-object tracking.

The methods for image-based tracking are used in several applications, such as action recognition, self-driving cars, security and surveillance, augmented reality apps, motion capture systems, and video compression techniques. In Augmented Reality (AR) apps, for example, if we want to draw a virtual three-dimensional object on a planar surface, we would want to keep track of the planar surface for a feasible output.

In surveillance or traffic monitoring, tracking vehicles and keeping records of number...