{"id":45828,"date":"2020-04-01T15:41:40","date_gmt":"2020-04-01T21:41:40","guid":{"rendered":"https:\/\/www.realsenseai.com\/uncategorized-cn\/realsense-openvino\/"},"modified":"2020-04-01T15:41:40","modified_gmt":"2020-04-01T22:41:40","slug":"realsense-openvino","status":"publish","type":"post","link":"https:\/\/www.realsenseai.com\/cn\/news-insights-cn\/realsense-openvino\/","title":{"rendered":"Introduction to the Distribution of OpenVINO\u2122 toolkit with the RealSense\u2122 Viewer"},"content":{"rendered":"

In today\u2019s software ecosystem, everyone seems to be leveraging some form of Artificial Intelligence (AI, also commonly referred to as Machine Learning) to perform analysis that was not traditionally performed well by a computer. More traditional software paradigms lack the capacity to properly describe complex interactions between a variety of different inputs.<\/p>\n

One usage of AI in particular is in the field of human vision, or the approximation of human-like vision. Object recognition and inference can be used in many ways to enable new and exciting capabilities. A hospital computer can diagnose breast cancer with high accuracy, by replacing previous high cost human labor with specially trained software. A drone can navigate and orient itself based on classification of objects (person, car, building, etc.) from visual inputs, or fly along electricity lines identifying problems like overhanging vegetation. The store you just walked out of could track the items you chose and charge you accordingly, without the need to check out. Your computer at home could identify your mood and play an appropriate soundtrack. AI imitates the way our brains work by setting up and training neural networks for specific tasks, similar to how we might practice a new skill by repeating it over and over.<\/p>\n

The Distribution of OpenVINO\u2122 toolkit, or \u201cOpen Visual Inference and Neural network Optimization\u201d and, as its name suggests, is an optimized convolutional neural network (CNN) implementation designed to run on hardware (CPU, GPU, or some other accelerator). It can leverage custom optimizations that provide added scalability and directly translate to increased throughput and performance. When your model incurs a heavy workload, every additional frame per second counts, and that\u2019s where the OpenVINO\u2122 toolkit shines.<\/p>\n

The OpenVINO\u2122 toolkit and RealSense\u2122 Technology<\/h2>\n

AI works on models that are trained on specific data. The more data, and more varied the data, the better the accuracy of the resulting model. Today, visual models are typically trained on a set of images that contain the appropriate subject matter: faces if you want to train facial features, etc. However, all these images are typically two-dimensional without any depth information to give additional cues. Like a brain working with two dimensions instead of three, depth needs to be inferred or even assumed in order to make any further determinations. How can you differentiate between a person and a picture of a person?<\/p>\n

RealSense\u2122 cameras and technologies are particularly well suited to enable a third dimension of input into AI models, enabling capabilities that did not exist before. The OpenVINO\u2122 toolkit supplies inference that is optimized and enables more complex models that provide more accurate results in realtime, RealSense\u2122 products provide the necessary information needed to actually make decisions, and without which such decisions are mere approximations. The two are complementary and both are critical to providing a good experience.<\/p>\n

The Viewer<\/h2>\n

RealSense\u2122 SDK\u2019s Viewer has long been one of the best demonstrations of the technology, able to immediately make clear its added value. We wanted to introduce the OpenVINO\u2122 toolkit into the Viewer as a vehicle for future functionality and a way of showcasing some of its capabilities at the same time.<\/p>\n

The end-result is a post-processing engine that can take the RGB frames from the RealSense\u2122 camera, run them through the Distribution of OpenVINO\u2122 toolkit and show faces, with gender and age, for the people it detects:<\/p>\n

\"Intel
RealSense Viewer with the OpenVINO toolkit at IROS 2019, Macau<\/figcaption><\/figure>\n

The addition to the viewer is a simple post-processing block for RGB frames that can be turned on or off at will, as seen in the screenshot below.<\/p>\n

\"Intel<\/p>\n

This post-processing block does the following:<\/p>\n