Video Surveillance as a Service (VSaaS), simply stated, is a cloud-based video surveillance solution that is packaged and delivered as a service over the internet. The price varies depending on the features of the plan (i.e. number of cameras, amount of storage, software features, etc.), and customers pay a monthly subscription price to use it. Internet Protocol (IP) cameras are installed at site locations, and the video is captured and streamed to a service provider’s data centre via an internet connection.
The video management software (VMS) runs on backend infrastructure provided by the service provider’s cloud. All video processing is done in the cloud, and all that is required to view the footage is an internet-connected device and a web browser.
Implementation of AI and deep learning
The cloud facilitates implementation of artificial intelligence and deep learning in the video surveillance marketThe cloud also facilitates implementation of artificial intelligence (AI) and deep learning in the video surveillance market. One of the major challenges with developing deep learning-based applications is access to real-word data and the ability to train the applications to work in any environment.
Companies need access to relevant datasets that need to iterate their solutions quickly. Cloud-based solutions are of significant advantage in this case, as they allow for continuous updates and easy collection of vast amounts of data.
“We will see the continued adoption of cloud-based intelligent video solutions that aggregate business data through video and artificial intelligence,” says Andreas Pettersson, CEO of Arcules, one of our Expert Panelists. Leveraging AI and IoT technologies with video data is becoming more common as organisations strive to optimise business operations while also boosting security across their facilities.
Actionable intelligence gathering
“The possibilities for this level of actionable intelligence gathering is endless, as markets such as hospitality, manufacturing, retail and SMEs that have multiple locations to manage, look to make sense of video in intuitive, streamlined ways,” says Pettersson. “The ability for technology to aggregate and analyse video surveillance and connected sensor data, identify trends in that data, and apply predictive analysis in businesses will have a huge impact in the coming year.”
Cloud systems can solve more problems than ever using artificial intelligence and machine learning
Cloud systems can solve more problems than ever using artificial intelligence and machine learning, and the capabilities expand way beyond video analytics to include analytics in general, crunching a variety of data provided by Internet of Things (IoT) sensors.
Another area of interest related to video in the cloud is the development of ‘smart codecs’ that security camera manufacturers are developing and marketing as a solution that goes beyond H.264 and H.265.
Maximising video storage
Customers are realising that cloud implementations are more cost-effective, easier to deploy and maintainThe ongoing need to better manage network bandwidth usage and to maximise video storage is further turning the emphasis toward smart codecs, which lend themselves to cloud applications. Extremely high-resolution video can now be moved around in a fraction of the time and solves the issue of transporting many cameras over constrained WAN connections.
With the elastic computing power available in the cloud, one can now envision a time where cloud computing costs could be low enough for the masses of video security solution use cases to be solved.
Customers are realising that cloud implementations are more cost-effective, easier to deploy and maintain, and in many cases, even more secure than traditional on-premise deployments. In partnership with the leading cloud providers, the security industry can carry this message to the customers and will gradually see a shift in the acceptance of cloud-based solutions in the traditional security markets.