Artificial intelligence poised to play a significant role in improving occupant safety in today’s commercial office buildings.
Advancements in chip architectures and facial recognition technology are now making it possible for AI-based video analytics software to be used to manage huge volumes of video data to enhance the safety and security of people in buildings and businesses.
The majority of video surveillance today is done manually with security personnel simultaneously attempting to view multiple video screens to identify anything that might represent a security issue.
While it is easy to identify an intruder or explosion using this method, it’s often difficult to identify theft, unauthorized entry by an intruder or the delivery of the suspicious packages because many of these incidents go unnoticed in traditional surveillance systems.
The inability to effectively identify these kinds of threats inhibits security personnel from taking pre-emptive actions which are often necessary to prevent public harm.
The volume of data and manpower required to effectively monitor buildings today is reaching unmanageable levels making the provision of security both costly and less inefficient.
The costs of hiring more security personnel to manage an increasing number of security cameras are often too high while the ability for the human eye to comb through video surveillance to find anomalies is extremely time-consuming.
Artificial intelligence enables better management of voluminous video data
The amount of video surveillance data has become so massive that law enforcement systems are unable to cope with the volume and vital information often is missed because the majority of the video is never viewed.
Now, through the use of artificial intelligence video analytics, security in commercial office buildings and campuses can be significantly improved.
AI-based security surveillance systems can serve as a virtual security guard with unlimited capacity and endless attention spans capable of monitoring videos 24 hours a day seven days a week.
Today a number of security companies Aegis AI, Evolv Technology, Deep Sentinel and others provide automated video and facial recognition technologies designed for airports, large venues, stadiums, offices and other public places that can significantly enhance security and public safety.
Machine learning algorithms can be trained and customized for unique environments and circumstances based on inputs such as facial features, detect zones, masks and camera angles.
For example, Chicago-based startup Aegis AI has developed a system capable of detecting a gun-in-hand adding a layer situational awareness to its machine learning algorithm which alerts first responders in emergency situations in schools, office buildings, and other public locations.
When a machine based AI spots suspicious activity its send alerts for human verification to security personnel who then review and assess risks and decide the best course of action.
While training algorithms to detect situational awareness is not an easy task, today self-learning and deep learning systems used to identify objects have matured to an advanced level and are enabling the use of AI technology in security systems across the world.