AI-based recognition systems, and smart security video networks have led to a paradigm shift in the architecture of security video systems.
Video security is not what it used to be. The data-driven tech revolution that has swept across the industrial landscape has also transformed the video security sector, leaving both visible and invisible changes. One of the clearly visible changes has been the rapid evolution of camera devices.
The days of fixed type cameras have long given way to a vast range of sophisticated equipment — pan-tilt-zoom cameras, which can follow a target across the scene, change angles and even zoom in on a face; thermal imaging cameras for 24×7 security, from bright light to dark nights;body-worn cameras used by security personnel, army and police personnel; drone cameras used in a multitude of applications from wedding photography to border security; licence plate recognition cameras that can identify and flag traffic violations; and many more.
An even greater impact on the security video industry has been the advent of internet protocol (IP) cameras or connected cameras. These cameras stream data (either continuously or motion triggered) to cloud servers. The opportunities presented by this high level of data generation have led to significant shifts in the video security field.
Intelligent video security systems
When it comes to security, time itself can be the enemy. Security considerations call for real time analysis of the video data and that is what smart cameras deliver. The camera ‘understands’ the scene and takes action. If it’s an intruder, the camera is able to zoom to capture more detailed footage.
If it’s a thermal hotspot in an unoccupied plant, the camera triggers a fire alarm. Smart security cameras do more than stream a static video feed; they are connected to sensors, they have embedded intelligence and storage, and even processing capability within the device itself. In addition, these devices enable a rich stream of video data that can be used in real-time for valuable and actionable insights.
This level of real-time computing calls for a blended architecture where the data goes through both cloud-based as well Edge-based analytics. (Edge-based analytics refers to the process of analysing data at the device level or the edge of the network.) Edge-based analytics on the security feed can help counter the issue of latency and enable rapid responses.
AI and predictive analytics
Deep learning and AI technologies have improved the capabilities of video security systems and allowing them to become exceptionally intelligent.AI-based systems enable cameras to store and process data based on pattern recognition of specific shapes, colours, sounds, vibrations, temperature, chemicals, pressure and so on. Here the role of data becomes critical. Data that is captured at the ‘edge’ is called fast data as it enables faster analytics. Facial recognition in smartphones, pattern recognition in autonomous vehicles, these are some of the contemporary usages of fast data and video data analytics.
Smart security video cameras generate a huge amount of fast data and need to be supported with embedded storage and data processing. Such advanced security video solutions require specialised storage that delivers high data access speed with enhanced durability.
What you see is what you get. That is why the demand for advanced image analytics has led to an increasing trend towards HD and Ultra High Definition (UHD) video formats. The efficient use of AI-based analytics for pattern recognition demands a higher level of resolution, which is made possible by UHD. This in turn has an impact on the design and architecture of the data storage infrastructure and the network capability to stream such high quantum of data. UHD video requires higher storage capabilities at device, platform and server levels.
Although security is still the most common end-use of video security, the technology is being increasingly used to gain competitive advantages in business. Retail enterprises, for instance, can analyse data from video streams for insights on consumer browsing and purchase behaviours. This is a rich and minable stream of data that can be leveraged to help enhance the retail experience. In industries such as manufacturing and healthcare, image recognition cameras can help maintain operational control, for instance, by motion-triggered cameras that identify movement in restricted areas. In urban transportation, smart cameras can help identify incidents and aid in improving the flow of traffic or pedestrians, thus keeping city streets safer.
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