While video analytics has long been a staple of the security industry, advances in technology and leaps in imagination are taking it well on the far side of traditional safety and security applications.

Nowadays you’ll notice video analytics being applied to a host of operational tasks in business, education, health care and more. As users become more accustomed to technology, they’re beginning to use analytics in some amazingly clever ways.

However, before we discuss how analytics are being employed, let’s take a step back and think about what’s creating all of this innovation doable.

THINK about image processing and storage

With every generation of the video camera, we’ve seen substantial enhancements in image resolution, frame rates, light sensitivity, and even dynamic range. thus were able to capture way more detail than ever before. At an equivalent time, we’ve seen corresponding enhancements in compression algorithms to scale back bandwidth consumption and storage. Video analytics each enhances and helps us bring structure and sense to that mountain of video data we collect so we are able to quickly zero in on the foremost relevant footage. Video analytics also can be accustomed to directing the cameras to record and send the sole video that’s of interest, further reducing bandwidth and storage need.

THINK about open application platforms

Video cameras that support an open platform are ready to give a framework for third-party programmers to develop and embed custom analytics directly on the camera. additionally, the open platform allows multiple cameras to integrate with input from different technologies like access control, environmental sensors, radar detectors, and even network audio systems for a more holistic approach to monitoring, verifying, and responding to events.

THINK about metadata and intelligence

It all comes all the way down to metadata (the content information) generated by the camera. whereas the video is unstructured data, video analytics will use the metadata to provide context to the visual pictures and organize them in a method that creates them easy to search out, perceive and use. This has been invaluable in serving users to quickly glean actionable intelligence from what they see. basically, video analytics turns cameras into smart observers – what several would call a processor with a lens. currently, they’re able to observe, recognize and classify objects and ascertain attributions like speed, direction, color, and size. More refined analytics will even distinguish demographics like age, sex, and behavioral patterns. There are also analytics that use sentiment analysis to spot a person’s moods like happiness, sadness, and anger. This ability to aggregate and parse data in numerous ways is what makes video analytics an excellent tool with endless possibilities.

THINK about deep learning and artificial intelligence (AI)

Today we’re beginning to see a fusion between camera vision, which isn’t any longer simply a lens, and deep learning technologies that are sanctioning video analytics to become more accurate in extracting, classifying, and cataloging metadata, smarter at tracking patterns, and trending demographics, distinguishing hot spots and remodeling video into usable intelligence. We’re seeing acoustic analytics learning over time about the environment during which they’re deployed thus they’re higher able to distinguish the distinction between say a door slamming, a car backfiring, or a gunshot; between a window breaking and a drinking glass shattering; between verbal aggression and exuberant conversation.

Feeding all that analytics information into AI engines not only provides us with an intelligent body of actionable data however serves as a basis for predicting future trends, patterns, and behaviors, that helps us improve our own decision-making over time.

These different ways that analytics can be applied to become a springboard for your own ideas on how to achieve a greater return on your own analytics investment.