New-Tech Europe | February 2019

or identify persons of interest and share the visual and analytics data within networks. When it comes to self- driving cars, embedded vision steers the vehicle within its lane on the road and avoid obstacles that may appear without any warning. This example highlights the importance of Embedded Vision in this application to not only to see but to understand the scene and react accordingly. From a technical perspective, a smart embedded vision system not only recognizes defects or abnormalities based on pre-defined criteria, it is capable of determining an appropriate response to correct or avoid them. Embedded Vision provides a more comprehensive view of the world by recognizing, understanding and identifying the environment without further external interaction. What is required for a fully embedded vision product? It is all about efficiency. Embedded Vision brings vision technology to its simplest formula “capture, process, respond”. The building blocks of a true embedded vision systems are: Sensor or Sensor Module Control unit to receive the images and direct them to the processing unit Processing unit that is either local or cloud based that provides the full image pipe line Purpose build algorithms and neural networks that provide the intelligent processing of the images Embedded Vision requires more analysis and processing of the image data, so an embedded vision product

Classic Vision Separated system for capturing image streams and processing, outsourced analysis for decisions, made to support many possible tasks. Embedded Vision Fully integrated all-in-one system capturing image streams, on-board processing and interpreting data, autonomous decision making, resulting in an action. Made for a specific task. How IoT applications benefit from Embedded Vision Embedded Vision does not only take pass / fail or yes / no decisions based on some criteria, it provides a broader form of intelligence leveraging neural networks that massage and analyze the image data and information. Embedded Vision systems moves from pre-defined actions based on specific inputs to specific reactions to spontaneous situations, with real- time decision making and resulting activities. This is similar to how smart cameras work but allow for adaptation and expansion with evolving responses as more scenarios are encountered and evaluated. Also, the “smarts” are being deeply integrated into every kind of device, more and more. This creates new IoT devices that are more aware and better able to process inputs from their surroundings, further propagating how Embedded Vision is enabling more VoT devices. Most industrial and consumer products are internet-aware today, exchanging data with one and other using local networks and the cloud. With the addition of vision, these devices will be controllable via eye tracking, face or gesture recognition. As an example, a refrigerator with embedded vision inside would be able to recognize what food has been consumed and then automatically add it to the family’s online shopping list. Using intelligent embedded vision, security applications can count people, create heat maps,

typically includes back-end processing that is done on either an ISP or GPU. Intelligent algorithms running on these devices allow the machine to analyze the incoming video data, process them and interpret them to make decisions and react accordingly. Embedded vision products provide not only data but results based on this data. “Embedded Vision does not have to be small but it has to be smart.” – Darren Bessette Typical embedded vision components come in small form factors, but this does not mean that they can only be used in small or low-cost devices. As with the example provided previously, self-driving cars are the exception to using embedded vision in small, low cost devices. A better way to think of embedded vision is to think of smart imaging that do not need human interaction to process and react to the video stream. Embedded Vision is enabling machines to see and think, powering the Vision of Things of the future. Figure 1: Embedded vision systems typically combine a camera, processing device and interface

Darren Bessette, Category Manager and machine vision expert, FRAMOS

Embedded vision examples: Home robotics, e. g. robot vacuum cleaners

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