It’s helpful to reflect on where we are now versus where we are going. Today, there is still more discussion about what might be possible than actual physical products on the market. Much of the conversation centers on practical ways to utilize deep learning and neural networks and how these techniques can improve analytics and significantly reduce false-positives for important events. When talking to end users about analytics, it becomes clear that many still don’t take full advantage of analytics. In some cases, this can be due to a history of false-positives with previous generation technology, but in others, it can simply be a case of not believing that reliable analytics are achievable for their unique needs. The good news is that with AI, or more accurately, deep learning and neural networks, we are going to a new level of enhanced analytics and data gathering in two key areas:
Analytics can run on a dedicated server or on the edge inside the camera. Server-side AI will be used when more heavy-lifting is required such as large database comparisons typical in facial recognition, ALPR and more. However, even for compute-intensive tasks, efficiency both in processing speed and reduced bandwidth requirements can be found by using a hybrid approach with edge devices and servers working together. AI-derived metadata from the edge can be sent to a server-side application as opposed to sending raw video which requires that the server decode multiple streams just to run an analysis.