The term “embedded vision” refers to the use of computer vision technology in embedded systems. Stated another way, “embedded vision” refers to embedded systems that extract meaning from visual inputs.
Similar to the way that wireless communication has become pervasive over the past 10 years, embedded vision technology is poised to be widely deployed in the next 10 years. Vision algorithms were originally only capable of being implemented on costly, bulky, power-hungry computer systems, and as a result computer vision has mainly been confined to a few application areas, such as factory automation and military equipment.
Today, however, a major transformation is underway. Due to the emergence of very powerful, low-cost, and energy-efficient processors, image sensors, memories and other ICs, it has become possible to incorporate vision capabilities into a wide range of embedded systems.
Similarly, OpenCV, a library of computer vision software algorithms originally designed for vision applications and research on PCs has recently expanded to support embedded processors and operating systems.
Intel started OpenCV in the mid 1990s as a method of demonstrating how to accelerate certain algorithms. In 2000, Intel released OpenCV to the open source community as a beta version, followed by v1.0 in 2006. Robot developer Willow Garage, founded in 2006, took over support for OpenCV in 2008 and immediately released v1.1. The company has been in the news a lot lately, subsequent to the unveiling of its PR2 robot.
OpenCV v2.0, released in 2009, contained many improvements and upgrades. Initially, the majority of algorithms in the OpenCV library were written in C, and the primary method of using the library was via a C API. OpenCV v2.0 migrated towards C++ and a C++ API. Subsequent versions of OpenCV added Python support, along with Windows, Linux, iOS and Android OS support, transforming OpenCV (currently at v2.3) into a cross-platform tool. OpenCV v2.3 contains more than 2,500 functions.
What can you do with OpenCV v2.3?
Think of OpenCV as a box of 2,500 different food items. The chef’s job is to combine the food items into a meal. OpenCV in itself is not the full meal; it contains the pieces required to make a meal. But the good news is that OpenCV includes a suite of recipes that provide examples of what it can do.
Experimenting with OpenCV
If you’d like to quickly do some hands-on experimentation with basic computer vision algorithms, without having to install any tools or do any programming, you’re in luck.
BDTI has created an easy-to-use demonstration package that allows anyone with a Windows computer and a web camera to run a set of interactive computer vision algorithm examples built with OpenCV v2.3.
You can download the installer zip file from the Embedded Vision Alliance website, in the Embedded Vision Academy section (free registration is required). The installer will place several prebuilt OpenCV applications on your computer, and you can run the examples directly from your Start menu. BDTI has also developed an online user guide and tutorial video for the OpenCV demonstration package.
Examples named xxxxxxSample.bat use a video clip file as an input (example video clips are provided with the installation), while examples named xxxxxWebCamera.bat use a video stream from a web camera as an input. BDTI will periodically expand the OpenCV demonstration tool with additional OpenCV examples; keep an eye on the Embedded Vision Academy section of the Embedded Vision Alliance website for updates.
Developing apps with OpenCV
The most difficult part of using OpenCV is building the library and configuring the tools. The OpenCV development team has made great strides in simplifying the OpenCV build process, but it can still be time consuming.
To make it as easy as possible to start using OpenCV, BDTI has also created the BDTI Quick-Start OpenCV Kit – a VMware virtual machine image that includes OpenCV and all required tools preinstalled, configured, and built. The BDTI Quick-Start OpenCV Kit makes it easy to quickly get OpenCV running and to start developing vision algorithms using OpenCV.
The BDTIQuick-Start OpenCV Kit image uses Ubuntu 10.04 as the operating system (Figure 1 below). The associated Ubuntu desktop is intuitive and easy to use. OpenCV 2.3.0 has been preinstalled and configured in the kit, along with the GNU C compiler and tools (GCC version 4.4.3). Various examples are included, along with a framework so you can get started creating your own vision algorithms immediately.
The Eclipse integrated development environment is also installed and configured for debugging OpenCV applications. Various Eclipse OpenCV examples are included to get you up and running quickly and to seed your own projects. And four example Eclipse projects are provided to seed your own projects.
Figure 1: The Ubuntu Desktop installed in the BDTI VMware virtual machine image
A USB webcam is required to use the examples provided in the BDTI Quick-Start OpenCV Kit. Logitech USB web cameras, specifically the Logitech C160 in conjunction with the free VMware Player for Windows and VMware Fusion for Mac OS X, have been tested with this virtual machine image. Be sure to install the drivers provided with the camera in Windows or whatever other operating system you use.
To get started, download the BDTI Quick-Start OpenCV Kit from the Embedded Vision Academy area of the Embedded Vision Alliance website (free registration required). BDTI has also created an online user guide for the Quick-Start OpenCV Kit .