Issue link: https://resources.mouser.com/i/1499313
8 BRINGING INTELLIGENCE TO THE EDGE Embedded vision is one of the leading technologies, with embedded AI used in smart endpoint applications in a wide range of consumer and industrial applications. There are a number of value-added use cases examples, such as counting/analyzing the quality of products on a factory line, keeping a tally of people in a crowd, identifying objects, and analyzing the contents of a specific area in the environment. While considering the processing of embedded vision applications at the endpoint, the performance of such an operation may face some challenges. The data flow from the vision- sensing device to the cloud for the purposes of analyzing and processing could be very large and may exceed the network available bandwidth. For instance, a 1920×1080px camera operating with 30 frames per second (FPS) may generate about 190MB/s of data. In addition to privacy concerns, this substantial amount of data contributes to latency during the round trip of data from the edge to the cloud, then back again to the endpoint. These limitations could negatively impact the employment of embedded vision technologies in real-time applications. IoT security is also a concern in the adoption and growth of embedded vision applications across any segment. In general, all IoT devices must be secured. A critical issue and concern in the use of smart vision devices is the possible misuse of sensitive images and videos. Unauthorized access to smart cameras, for example, is not only a breach of privacy, but it could also pave a way for a more harmful outcome. Vision AI at the Endpoint • Endpoint AI can enable image processing to infer a complex insight from a huge number of captured images. • AI uses machine-learning and deep-learning capabilities within smart imaging devices to check a huge amount of previously well-known use cases. • For optimum performance, embedded vision requires AI algorithms to run on the endpoint devices and not transmit data to the cloud. The data here is captured by the imaging recognition device, then processed and analyzed in the same device.