Think of it this way: to centrally process analytics on a 20-camera server deployment, a campus would need a very high-end processor, like a quad core Xeon. By comparison, if they handle analytics at the edge, all they’d need is a Core 2 duo, which is really a desktop motherboard. The cost differential could be several thousand dollars. There are the less obvious costs as well: PC warranties are generally cheaper than server warranties and it costs more to service a server than it does a PC.

Finally, there’s a green element to all of this. If you put analytics on the server, you need more energy to power and cool the equipment. With in-camera analytics, you’re reducing your power consumption and saving BTUs on air conditioning.

Kattan: We have seen that the adoption of IP technologies in general has accelerated the adoption of video analytics. In most professional camera installations, there is a need to record the video and store it for certain periods of time. The ability to implement the analytics in the camera can actually smooth the process of pre-processing, or tagging, relevant video segments, and in some cases, reduce the flow of “uneventful” video over the network. The ability to store only relevant video segments typically saves tens of thousands of dollars in storage costs over the lifetime of a surveillance system.

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Q: Nilsson: Being able to tag and quickly retrieve video footage is essential. In an emergency or criminal situation, it can help drastically reduce the time of suspect apprehension from weeks to hours or even minutes. Specific to the education and healthcare communities, what are some of the more commonplace uses for video analytics?

Kattan: At the most barebones level, many campuses use basic motion detection – monitoring building perimeters to protect against breaches to the premises and property. The analytics can help security personnel detect crowding, which may indicate a fight, an injury or safety hazard.

In some cases, video analytics are used to spot individuals engaged in potentially harmful or illegal activity in or around campus. We’ve seen schools use analytics to trigger an alert if a camera detects someone approaching a perimeter fence when another person is standing on the other side since this might indicate the transfer of contraband, such as drugs, alcohol or weapons.

Hackett: Video analytics also helps security personnel spot suspicious individuals loitering near the property, especially during vulnerable times of the day like drop off and pick up. This is especially pertinent for K-12 environments where administrators are held liable for students’ whereabouts once they enter the school grounds.

Another big issue is sending alerts when cars are discovered blocking fire or bus lanes or objects are obstructing emergency exits. A number of campuses tie video analytics into their access control systems, using the intelligence to direct cameras to track suspicious individuals as well as send staff an alert message on their BlackBerry or other mobile device with an appropriate snapshot of the scene.

This could help prevent admittance of a disgruntled former employee or student who is restricted from campus. If an emergency situation does arise, some institutions have set analytic thresholds to allow local law enforcement access to the video streams. This ensures that first responders have full situational awareness before they arrive on the scene.

Q: Nilsson: Besides safety and security, I’ve heard from a few of our education-focused integrator partners that colleges and universities are quite creative when pushing the limits of analytics. What are some of the more innovative ways you’ve seen video analytics being applied?

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