10 Factors to Analyze Today’s Video Analytics Options
Let’s take a look at how installing security contractors can work with clients to recommend and design the appropriate video analytics solution.
In remote or isolated locations, such as a substation, cell tower or train depot, deploying excessive hardware - especially servers - doesn't make sense as these locations often aren't easily accessible to maintenance crews.
2) Self-sufficiency
In remote or isolated locations, such as a substation, cell tower or train depot, deploying excessive hardware – especially servers – doesn’t make sense as these locations often aren’t easily accessible to maintenance crews. By placing the video analytics on the edge, however, customers can rely on the self-sufficiency of the camera, which can do everything, including recording to its embedded storage. In this case, even if the network failed, the analytics on the camera would still run as normal.
Server-based analytics in this kind of environment aren’t self-sufficient simply because they wouldn’t function independently in the case of a failure or mishap. And given the typical characteristics of remote, isolated locations, that’s bound to happen.
3) Reliability
Does the organization care more about processing power or video quality? Obviously an IP camera on the edge has less processing power than a server, but sometimes it’s not enough to handle video analytics applications. Note that this can be compensated for with an onboard processor. On the other hand, cameras have access to raw, uncompressed, high-quality video, which in turn enables high reliability.
Reliability only becomes an issue if the camera’s embedded processor is not powerful enough. Processing power for servers is a nonissue; they have plenty of it. Customers can even reduce overload by defining a maximum number of cameras per server. Server-based solutions are very reliable; the only issue is the potential for poor-quality incoming video streams.
4) Bandwidth
Again, this depends on an organization’s infrastructure. With low-bandwidth connections, analytics on the edge is ideal. Analytics takes place within the camera, and when relevant, it sends an alarm to the center. Bandwidth isn’t much of a concern. With server-based analytics, the incoming video stream needs to reach a certain quality level in order for the video analytics to be able to effectively process the captured video. As quality is determined by frame rate and resolution, bandwidth is crucial.
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5) Substitution capability
A server-based model offers more flexibility to substitute or switch out the analytics as performance needs shift. Companies can just replace the software on the server with more effective analytics. When analytics are embedded within the camera, however, the process is more problematic. Most manufacturers use one type of analytics across their portfolio, so changing analytics software most likely means replacing every camera with a new brand.
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