Intel offers a range of hardware options for IoT deployments. Computing power progresses in order from the Intel Atom® processor,to the Intel® Core™ processor and finally to the Intel® Xeon® processor. As IoT demand drives increases in data volume, a more powerful processor is required, as well as additional storage.
Learn how to use OpenCV* to count people using edge detection rather than using server farms.
What is OpenCV and how it applies to IoT. An overview of OpenCV and what Intel offers for developers in that space.
1. Robots and ASTRO
In support of computer vision development efforts, we've created a GitHub* repository of twelve computer vision code samples. These code samples are a good starting point for developers who wish to develop more robust computer vision and analytic solutions. We use the Retail, Digital Signage market in these examples but the technology can be used in a variety of different markets.
This sample application takes an image or frame of an analog gauge and reads the value using computer vision. It consists of two parts: the calibration and the measurement. During calibration, the user gives the application an image of the gauge to calibrate, and it prompts the user to enter the range of values in degrees. It then uses these calibrated values in the measurement stage to convert...
OpenCV, Python* This sample application is useful to see movement patterns over time. For example, it could be used to see the usage of entrances to a factory floor over time, or patterns of shoppers in a store. color/heat map background subtraction
Learn how to run computer vision inference faster on Intel Architecture using the Intel® Computer Vision SDK Beta R3. This tutorial will walk you through the process of generating the files needed for the Inference Engine from a Caffe model, and how to run the Inference Engine in a C++ application. The source code for this tutorial is available on GitHub.