Learn how to use OpenCV* to count people using edge detection rather than using server farms.
This paper addresses how the Smart Video (SV) system architecture is increasing in complexity and evolving into new industries and use cases.
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.
With the amount of continuously generated data on the rise, the cost to upload and store that data in the cloud is increasing. Data is being gathered faster than it is stored and immediate action is often required. Sending all the data to the cloud can result in latency and presents risks when internet connectivity is intermittent. Edge computing involves processing data locally for immediate...
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
This paper provides guidance from the dictionary to appropriately define and discern the terms object detection, object recognition, and object tracking. We then explore some of the algorithms involved with each process to underpin these definitions.
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.