The case for local intelligence in IoT-centric surveillance devices

By _Ceva's Experts blog

November 23, 2015

The network-centric security and surveillance industry enabled by the IP-based cameras has been steadily progressing over the years. Now the advent of the Internet of Things (IoT) promises to turn this segment into a mass surveillance infrastructure. However, the crossover between IoT and surveillance is also demanding the edge devices like security cameras to get connected as well as get smart.

In other words, move more imaging and video analytics to camera and process information directly inside the smart camera. So, in this facet of IoT, where surveillance machines are becoming part of the network of connected devices, it is imperative that edge devices like security cameras acquire some level of intelligence while some of the data is sent to the cloud servers.

Take the use case of object recognition in the context of home security and surveillance. First, an object, for example, a person is recognized. Next, the camera has to identify if the person is part of the list of approved people that have access to the home or building. Then, the camera must identify the situation; for instance, if the person has fallen or has entered a certain area that is prohibited for him.

So the camera system may simply create a notification in the form of a message or a call. Apparently, it's hard for the cloud to respond to all the data quickly enough because data transfer isn't always that fast. The data transfer in the cloud environment isn't real-time either, as some people might have believed. Sometimes, even the network link to the cloud is down.

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