Book Image

Rapid BeagleBoard Prototyping with MATLAB and Simulink

Book Image

Rapid BeagleBoard Prototyping with MATLAB and Simulink

Overview of this book

As an open source embedded single-board computer with many standard interfaces, Beagleboard is ideal for building embedded audio/video systems to realize your practical ideas. The challenge is how to design and implement a good digital processing algorithm on Beagleboard quickly and easily without intensive low-level coding. Rapid BeagleBoard Prototyping with MATLAB and Simulink is a practical, hands-on guide providing you with a number of clear, step-by-step exercises which will help you take advantage of the power of Beagleboard and give you a good grounding in rapid prototyping techniques for your audio/video applications. Rapid BeagleBoard Prototyping with MATLAB and Simulink looks at rapid prototyping and how to apply these techniques to your audio/video applications with Beagleboard quickly and painlessly without intensive manual low-level coding. It will take you through a number of clear, practical recipes that will help you to take advantage of both the Beagleboard hardware platform and Matlab/Simulink signal processing. We will also take a look at building S-function blocks that work as hardware drivers and interfaces for Matlab/Simulink. This gives you more freedom to explore the full range of advantages provided by Beagleboard. By the end of this book, you will have a clear idea about Beagleboard and Matlab/Simulink rapid prototyping as well as how to develop voice recognition systems, motion detection systems with I/O access, and serial communication for your own applications such as a smart home.
Table of Contents (15 chapters)
Rapid BeagleBoard Prototyping with MATLAB and Simulink
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Motion detection algorithm


Now we have the video input ready to use and the video display working as well. The next step is the heart of this chapter: how to deploy a motion detection algorithm following the rapid prototype development principle.

Firstly, let's spend some words to introduce the frame difference-based motion detection algorithm, which is employed in this chapter. As shown in the previous figure, the motivation is very simple. If we already know the background information—for example, if the background is stationary—then for each captured frame, simply subtracting the background image yields the regions of interest, that is, the motion area.

However, this method has several shortcomings. The most important is, how to get the background image? The best method is human aid. For example, the operator manually chooses a frame without any interesting object as the background image, which increases the operational complexity and uncertainty of the system. Even so, problems still exist...