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Need for local intelligence in IoT surveillance apps

Posted: 22 Dec 2015 ?? ?Print Version ?Bookmark and Share

Keywords:Internet of Things? IoT? analytics? surveillance? processing engines?

In the last couple of years, the network-centric security and surveillance industry enabled by the IP-based cameras has been steadily progressing. 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 recognised. 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.

Figure 1: IoT surveillance apps like scene analysis demand local intelligence inside cameras.

Birth of smart camera
Now consider another use case: Smart city. Image and video resolution are going up due to the proliferation of cheap cameras. But it also requires more bandwidth. There can be 1,000 cameras in a smart city, and it will take too much of bandwidth while they are all connected to the cloud. All of them will need to transfer, handle and store massive data quantities.

So moving more video analytics to the camera can reduce cloud server processing. Moreover, having local analytics power in end devices like surveillance cameras can limit data traffic. It's worth noting that cloud can't do everything no matter how good processing engines and algorithms are. Then, power could be concern for devices like smart meters, which will drain too much of power in transmitting all the data to the cloud.

There could also be regulation at some stage in the future about the cloud usage amid privacy issues. The monitoring of elderly at home, for instance, may simply require a phone call after the transmission of an alarm signal. On the other hand, people may hesitate to buy cloud-centric safety products because of privacy concerns.

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