Surveillance

In security and surveillance, we have already seen the positive impact of AI: for more accurate object detection and classification leading to more effective and faster forensic searches, reduced network storage and efficient bandwidth use. In summary, saving time and money and helping companies succeed.

Across our industry, we will continue to see more deployments of “smart” cameras and devices that combine AI and deep learning algorithms. The result will be more targeted forensic searches, enhanced operational efficiency, and minimized storage and bandwidth requirements.

In surveillance it is well-proven that AI, especially when combined with deep learning, can produce more accurate searches that notice only the elements a security team needs to see. That is simply a matter of increasing accuracy through AI algorithms.

One term we will see being discussed more when it comes to AI is “context awareness.”

It is already happening in other AI applications, for example, subtitling and captioning of movies or TV shows. Where basic AI models translate sentences one by one, context aware AI can “read between the lines” and accurately consider the “context” of a conversation, language nuances and the subtle differences of gender, slang and multiple word meanings.”

AI at The Camera “Edge ‘

Context awareness takes AI beyond the level of pre-configured algorithms. It gives systems the ability to gather information about the environment and adapt its behavior accordingly. Now, the camera is making choices to optimize its performance based on what it has learned in the past and what is important to the user.

When introducing AI into products a years ago, it was done on a selective and specific basis. We could detect a person or vehicle by specific attributes and trigger an event and start recording or initiate a forensic search.

Through ongoing firmware updates, newer models take that targeted AI detection and internally adjust what the camera is doing – again, using context awareness.

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