Putting the Video Analytics Idea Together
As you read above, these IVAs all orchestrate various algorithms to achieve and deliver results. Essentially though, IVAs detect for and determine if a defined event
or behavior has been found or occurs within a video camera’s field of view and then notifies the designated user of the finding.
In a similar manner, most of us go through varying processes depending on if we’re searching for keys at home or for our friend in a busy station.
Video Analytics Processing Power
Thinking about the entire process, could there be a single solution that can do everything effectively? It seems like an insurmountable amount of tasks: from
processing each single frame’s analytic data to displaying it together with the video, into creating a complete video system with an array of user selectable &
customizable IVA in a building or any other scenario, all the way to putting multiple systems together that report back to a central control center.
It’s not impossible. To demonstrate this, let’s look at what IVAR® from Gorilla
can do and how it operates.
CPU and GPU Video Analytics Processing
Video analytics as a whole requires a lot of dedicated processing power. We should keep in mind here that before optimization and edge devices with capable CPUs, video analytics was processing
both video and analytic data on one machine and required additional GPUs to do most of the work. Technology and the ability to split these two up has advanced to
the point that it’s now possible to keep the video data at the edge while pushing the analytic data up the network for quick processing.
One technology, which Gorilla was the first to adopt, is the Intel® distribution of the OpenVINO™ toolkit. Using the OpenVINO™ toolkit to optimize IVAR
keeps deployment and upkeep costs low while decreasing operating temperatures by minimizing the need for expensive GPUs.