This checklist provides a quick rundown of 10 critical factors to consider as you evaluate video analytics for your security system.
Video analytics is not standalone technology. Compatibility with network and video equipment is essential for optimum performance. Open, standards-based systems are mandatory.
2. Realtime operation
This seems like a no-brainer, but realtime monitoring and realtime alerts are crucial capabilities. Facing a potential security threat is no time to go to the videotape. You want your video analytics to detect the threat as it is unfolding, issuing an immediate alert so that proper action can be taken to avert the event.
3. Site-specific rules
Due to cost constraints, most video analytics systems offer a limited number of detections - usually just one, maybe two - per camera, and the same one or two detections for all cameras in the system. While you can select which one or two detections are installed, this does not allow for a flexible system. And just one or two types of detection just do not cut it in today's world. Most enterprise-grade organisations, both public and private, need to detect several different types of security threats in some locations and an entirely different set of threats at others.
4. Environmental conditions
Environmental conditions are another critical factor in successfully deploying video analytics. The system should be fully operable in both indoor and outdoor locations, in full daylight and in deepening shadows, under artificial lighting or glaring sunlight, under adverse and varying weather conditions. The software should compensate for background interference such as moving trees, and it should also include filters for shadows cast by moving or stationary objects, variable light levels, and random weather factors like clouds, rain, snow, ice and wind.
Beware. While current video analytics systems are far more accurate than their predecessors, accuracy claims bear close scrutiny. Virtually every video analytics provider claims that their system 'has the highest accuracy in the industry, with a 95+% accuracy rate in probability of detection (POD) and extremely low false alarm and nuisance alarm rates (FAR/NAR)'.
Sound familiar? The question to ask is under what conditions the measurements were taken? An ideal indoor set-up, with controlled lighting and no weather to mention? Or outdoors at twilight on a windy, rainy day...
Similarly, every provider claims that their system is scalable, allowing you to add new cameras - one at a time - expanding your system from 10 to 10 000 cameras.
It is true. But at what cost? In this claim, the video analytics software is far more accommodating than the supporting hardware.
In the typical system configuration, each surveillance camera transmits video to a central video server where the analytics software analyses the incoming video for security breaches and suspicious activity. What they do not tell you is that the typical server can only support a maximum of 16 cameras. So when you add the 17th camera, you need to add a whole new server - for just that one camera. It can be a budget-breaker when added to the other costs for a new camera, new video encoder, and supporting network equipment. So most organisations have a cost-effective threshold: they add the 17th camera along with 4-5 others that will justify a new server. Financially, it makes good business sense. Security-wise, it is risky - operating with compromised security until you reach that threshold.
Given that all video analytics systems are scalable, the issue is really whether the scalability is affordable. If adding just one camera is cost-prohibitive at varying stages of your expansion needs, then the system can hardly be described as scalable. The ratio of cameras per server is a major budget and expansion consideration.
Selecting the right cameras is as critical as selecting the right video analytics. Certainly you want a system that supports the full range of camera technologies - analog, digital, IP, IFR/thermal - so that it can be integrated with an existing surveillance system. But even as you introduce new high-end capabilities, you do not need the highest-end cameras in all locations. Match camera type to the specific circumstances of each location and the corresponding environmental conditions. By optimising camera costs you can afford more coverage. Besides, video analytics will carry the heavy load.
This is not a trivial point. Training time and costs can be a backbreaker in the security sector, where the annual personnel turnover rate easily runs 200% or higher. Look for a system that is intuitive to operate so that it is easily and quickly learned.
10. Security needs
Understanding your specific security needs is fundamental in determining what is the right video analytics package for your organisation. Look for the system that best matches your needs. Video analytics covers a lot of territory, ie, perimeter security, intrusion detection, graffiti and vandalism, people security, building security, moved or stolen object detection, unattended package or baggage detection, stopped vehicles, tailgating, crowd dynamics, etc. And the list is growing. Review current security policy and identify existing surveillance equipment. Physically inspect the facilities where you will be using video analytics: walk the property, and map out the weak spots.
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