Deep Learning to Push Surveillance Capabilities

By using powerful algorithms, devices are now capable of understanding and adapting to their environment by storing and analyzing large data sets, all without human intervention.

Deep learning is an emerging field currently gaining significant attention in the security industry, especially in the surveillance market. By using powerful algorithms, devices are now capable of understanding and adapting to their environment by storing and analyzing large data sets, all without human intervention.

Considered the next Industrial Revolution, the deep learning of artificial intelligence is making technology more efficient than ever thought possible. This intelligence is a key component to the development of sophisticated, data-rich information that enables surveillance systems to search through hours or days of video with a single click to locate an individual person or vehicle across a single site or multiple locations.

Deep learning provides the necessary building blocks to clearly identify a person and know if that person is walking, running or crawling — distinctions that video analytics was not previously capable of doing.

When deep learning devices are installed alongside loss prevention and point-of-sale monitoring, operators can manage inventory levels more effectively. With perimeter protection systems, devices with deep learning can significantly increase accuracy of alarm events and reduce alarms triggered by bad weather or wildlife.

As advancements in deep learning are made, we are moving closer to a safer, more intelligent world. By providing cost savings, as well as powerful security measures that once only existed in sci-fi films, deep learning continues to push the boundaries of technological capability.


Peter Ainsworth is the General Manager Commercial Products, Security at Johnson Controls.

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