Book Image

Machine Learning for OpenCV

By : Michael Beyeler
Book Image

Machine Learning for OpenCV

By: Michael Beyeler

Overview of this book

Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!
Table of Contents (13 chapters)

Detecting pedestrians in the wild

We briefly talked about the difference between detection and recognition. While recognition is concerned with classifying objects (for example, as pedestrians, cars, bicycles, and so on), detection is basically answering the question: is there a pedestrian present in this image?

The basic idea behind most detection algorithms is to split up an image into many small patches, and then classify each image patch as either containing a pedestrian or not. This is exactly what we are going to do in this section. In order to arrive at our own pedestrian detection algorithm, we need to perform the following steps:

  1. Build a database of images containing pedestrians. These will be our positive data samples.
  2. Build a database of images not containing pedestrians. These will be our negative data samples.
  3. Train an SVM on the dataset.
  4. Apply the SVM to every possible...