Video surveillance components such as data storage and camera management have moved into the cloud. Data connections that were once physical are today naturally wireless. However, fundamental systematic and technological changes only occurred in the recent past as cameras became smarter and data transmission faster.
Today cameras are much more than just the eyesight of surveillance systems.
The future: Smart video surveillance
Gartner predicts 5.8 billion enterprise and automotive IoT endpoints will be in use in 2020. A significant proportion of these are likely to be surveillance cameras used in various industries, such as retail, public transport and commercial buildings.
Today smart surveillance cameras add a huge value far beyond the security sector. In our blog you find many exciting examples of how cameras help make businesses not only safer but also more successful. Here are some examples that prove that the possibilities are almost unlimited:
Technology today is available much faster and easier to manage than just a few years ago. As a result, IoT cameras create businesses value much faster and with significantly less effort and risk.
Modern video surveillance ecosystems are highly flexible, enabling integrators and users to respond to fast moving environments in a timely and adequate manner. This also applies to IoT cameras, which, like smartphones, have become platforms for a variety of functions.
Characteristics of modern video surveillance systems
• Scalability: system components and functions can be easily changed and added.
• Ease of installation: implementation and testing of cameras and apps with just a few clicks.
• Usability: full control and simple management through online user interfaces.
One outstanding technology trend which system integrators can already use today to offer their customers innovative solutions is device empowerment: powerful processors and AI enable IoT cameras to do much more than just deliver plain video. A growing number of video analytics apps turn cameras into true business all-rounders.
• Real-time analytics (surveillance, face and object recognition, interpret events, alerting)
• Predictive analytics (combining retrospective and real-time analytics)
Video analytics apps run directly in IoT cameras, which previously happened in the cloud or on dedicated servers. In this way, surveillance cameras provide not only raw video data, but also ready-to-use analytics in a single operation.
Deep learning revolutionizes video analytics
A major challenge for developers of video analytics apps is to deliver the most accurate results possible. Where conventional algorithms reach their limits, AI and Machine Learning can help improve video analysis more reliable and efficient.
Example: distinguishing moving objects from people illustrates what smart cameras can do.