A camera or a computer: How the architecture of new home security vision systems affects choice of memory technology
A long-forecast surge in the number of products based on artificial intelligence (AI) and machine learning (ML) technologies is beginning to reach mainstream consumer markets.
It is true that research and development teams have found that, in some applications such as autonomous driving, the innate skill and judgement of a human is difficult, or perhaps even impossible, for a machine to learn. But while in some areas the hype around AI has run ahead of the reality, with less fanfare a number of real products based on ML capabilities are beginning to gain widespread interest from consumers. For instance, intelligent vision-based security and home monitoring systems have great potential: analyst firm Strategy Analytics forecasts growth in the home security camera market of more than 50% in the years between 2019 and 2023, from a market value of US$8 billion to US$13 billion.
The development of intelligent cameras is possible because one of the functions best suited to ML technology is image and scene recognition. Intelligence in home vision systems can be used to:
– Detect when an elderly or vulnerable person has fallen to the ground and is potentially injured
– Monitor that the breathing of a sleeping baby is normal
– Recognise the face of the resident of a home (in the case of a smart doorbell) or a pet (for instance in a smart cat flap), and automatically allow them to enter
– Detect suspicious or unrecognised activity outside the home and trigger an intruder alarm
These new intelligent vision systems for the home, based on advanced image signal processors (ISPs), are in effect function-specific computers. The latest products in this category have adopted computer-like architectures which depend for
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