Why you haven’t yet transitioned to AI for video surveillance
And how you should do it
Using Artificial intelligence to increase video surveillance performance is not new. Over the past few years, city governments, traditional security system integrators, and private enterprises have started to invest in AI for surveillance. But with the many benefits AI powered video analytics offers (analysis of 100% video flow, reduction in operator costs, decrease in safety risks), why haven’t all security sector actors jumped on board? The answer is due to the complexity surrounding a few major areas: scaling, integration with legacy systems, and adaptation to each client’s specific situational needs. Easy integration and a well thought out strategy for the usage of AI video analytics is necessary to reap the benefits of the technology, but it is not evident on how to achieve these goals.
AI Video Analytics must integrate and scale to your existing video surveillance operation
The scope of a video surveillance operation highly varies per client. In order to obtain the benefits of AI video analytics, the solution must adapt to the entirety of your legacy surveillance operation; including all cameras and smart cameras, sites surveilled, and existing software such as VMS.
Existing smart cameras, VMS, or servers are extremely costly and almost impossible to replace. A complete overhaul of legacy surveillance systems in favor of new smart cameras to perform AI video analytics takes years before any ROI is achieved. In addition, waiting for the natural turnover of infrastructure takes time and poses a threat to the survival of traditional surveillance companies.
The only way to generate ROI and remain competitive is through the integration of AI video analytics directly into existing surveillance networks, therefore minimizing costs and benefitting from the solution right away.
AI video analytics must adapt to your specific surveillance context to generate value
AI technology is not valuable in a silo. It is only the first step towards an enhanced video surveillance operation. Specific surveillance situations and challenges vary per client, therefore when adding AI to your current infrastructure, you must take into consideration diverse processes, contextualization, and integration methods in order to be successful. Examples of varying client contexts are intrusion management, fire-arm detection, or social distancing monitoring. In order for the AI solution to accurately identify these different events of interest, a collaborative effort between business experts, the client and data science teams is necessary to define a strategy on how to apply AI to a given surveillance operation. When AI video analytics is properly integrated and takes into account specific use cases, real added value such as cost reduction and increased security is achieved.
For more information about how AI video analytics can enhance your security operation visit www.ukpik.ai
