Artificial intelligence is an umbrella term for various data analytics processes. Machine learning is a subset of AI, and refers to a process that uses statistical techniques to give computers the ability to "learn" with data, without explicitly programming the computer to do so. And, deep learning is a subset, in turn, of machine learning, denoting processes based on deciphering the significance and meaning to be derived, if any, from the input data.
The beginnings of AI in home security systems
According to IHS Markit, there are just two applications where artificial intelligence is used currently in home-security systems.
The first is in systems integrated with consumer video cameras. Here video cameras use facial recognition functionality to identify whether the moving object is an intruder or a home member. Facial recognition in such a setting represents a basic form of machine learning, where a system analyses detected objects in the video footage against a pre-determined set of previously uploaded and approved images. Based on the results of the analytical process, the system sounds an alarm if the object doesn't match any of the images.
The second application in intruder alarm installations is when the system is integrated with a voice assistant. In this case, skills in the voice assistant have been developed with the use of numerous artificial intelligence methodologies to ensure smooth interaction between the user and the device.
Potential applications of AI in home security systems are myriad
Although current applications of AI processes in intruder alarm systems remain limited, many potential applications are possible that could give home security systems a much further boost, delivering even higher levels of security and comfort in the house.
In the future, IHS research shows that artificial intelligence can lead to the creation of a true smart home capable of learning the ways, habits, and preferences of the occupants of a home, automatically adjusting the settings in its various systems to accommodate both normal patterns and quirks in behaviour.
As more and more intruder alarm systems are integrated with home automation devices, genuine AI capabilities—where the system performs actions independent of the user's input—could create a truly smart and secure home in which security and comfort at home are greatly improved. In contrast, current home automation devices still rely on pre-determined scenarios that the user or installer must set up, following very specific criteria. Any changes to the settings must also be done manually by the system operator.
It could also actively monitor activity in the house and arm the systems at times when home members are out in the garden while the property remains unlocked and unoccupied for prolonged periods of time. If in such a circumstance a child were to run back into the house while the system is armed, the cameras could quickly recognise that it is not an intruder and not sound the alarm.
AI processes could be used by alarm receiving centres to create databases of all incoming alerts, which then could be analysed to predict false alarms. Initial pre-assessment of an incoming alert by the AI software could help the operator respond faster to alerts that are least likely to be false. At the same time, the software and system will be able to detect with near-accurate certainty the shape and form of a false alarm, helping to reduce costs and resources otherwise diverted to deal with false alarms.
Intrusion systems integrated with cameras can also help protect children against kidnappings. Cameras monitoring the area occupied by a child could sound an alarm upon the approach of the kidnapper, while also recording the kidnapper's image and sending it directly to a monitoring centre or to police authorities.
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