Video Technology and Artificial Intelligence

From hype to viable assistance system, the Regensburg-based company Dallmeier has been working on and with AI technologies for many years, and has now published four practical statements intended to help customers and providers to make a realistic assessment of video technology combined with Artificial Intelligence. When considering such solutions, much more than just the technology needs to be considered. At the beginning of a hype cycle, when innovations are being introduced, people often ignore the fact that new technologies always require public debate and changes to very real fundamental conditions before they can be implemented wholesale. The still unresolved problem in autonomous driving – when it comes to imminent accidents where the car has to make potentially fatal decisions – has become an almost classic example. There are similar unresolved questions when AI is used in video security technology: how much freedom to decide should a system be given? AI and video technology only function in a 'technologically holistic approach'. Technical systems are becoming more and more complex, which is why it is essential to evaluate all of the parameters that affect the performance of a complete solution. The IT axiom "garbage in, garbage out" is most apposite in this context: neural networks for classifying objects or processes or good facial recognition software can only deliver results that are consistent with the quality of the video image they receive. AI-based video analysis systems can only be as good as the camera systems that capture the images for them. There are already viable solutions which work well as 'assistance systems'. It goes without saying that Artificial Intelligence will play a decisive role in video technology – or may even become a core component of the discipline. Initial deployment scenarios and functioning solutions already exist, whether

in the optimization and analysis of analogue processes, for example at a casino gaming table, in the improved classification of objects for perimeter protection or in the assisted tracking of individuals in the context of urban surveillance. But a human is still at the center – the operator, the policeman, the forensic specialist. The market must learn to distinguish between functioning solutions and research projects. Every technical innovation is predestined to contend with ambiguous definitions, exaggerated expectations and variable interpretations of its capabilities: no-one really knows, but everyone involved has an opinion, which is why it is important to examine and question closely: which functions are market-ready and implementable, even if a little tweaking is needed, and what is still purely in the realm of research? Particularly with a view to strategic decisions and investments, prospective users should always begin by asking themselves whether a given result can be expected in twelve months, five years, or ever. Otherwise, they run the risk of losing sight of obvious solutions to pressing problems.