With the constant technological evolutions of new forms of video, the role of video analytics has never had more relevance. Broadly speaking, video analytics refers to the extraction of meaningful and relevant information from digital video. It’s primary concern is with the content of video. By building on research in computer vision, pattern analysis and machine intelligence, video analytics are able to detect people, objects and their surroundings and continuously analyse them, their movements & any suspicious behaviour. It has proved irrefutable in the worlds of surveillance, retail and transportation, providing security and safety and with the prospect of further developments on the horizon, their significance and importance can only grow.
How Video Analytics Work
For the majority of video analytic systems, they work in a series of processing steps. As a fundamental first step, the content needs to dissect what is happening in the video, frame by frame. Video analytic systems work on these two key concepts:
Motion Detection: By examining each pixel in the frame, the video analytics software is able to pick up even the slightest movement.
Pattern Recognition: Objects are distinguished within a frame. Specific objects/patterns can be programmed for recognition and will be recognised within the frame.
Once analyzed, the system then qualifies these changes in each frame, correlates qualified changes over multiple frames, and finally, interprets these correlated changes. Should any change happen, i.e. object is moved, goes missing, or new object added, the software immediately recognizes it and sends out an alert.
The Benefits of Video Analytics
Video analytics offers many benefits to a new or existing CCTV system. Video surveillance is a tricky and time-consuming business, and keeping track of everything that is going on requires a lot of manpower. Video analytics allows you to minimise the hassle that goes into 24/7 surveillance. Through the use of sophisticated algorithms & pixel by pixel analysis, they can pick up on the smallest of details. Analytics filters can be intelligently tailored to meet specific security or business needs as well, making them even more efficient for your needs.
The Limitations of Video Analytics
Whilst video analytics undoubtedly holds great promise and has already demonstrated it’s worth in a number of areas. That said, the world of video is constantly evolving and this is true of video analytics too. The expectations of video analytics seem to be greater than what can realistically be delivered. Pattern recognition and object/motion detection are all achievable however the extent to which they can prevent and solve incidents is prone to exaggeration. For example, if a user wishes to identify unattended baggage in a busy railway station at peak hours, the system will end up generating too many false alerts and the readings will not be reliable.
There is also a problem with their accuracy. License plate recognition has been around a long time and is well proven, however it is still not 100% accurate. Face recognition is notoriously difficult to perform reliably, and is extremely easy to get around and for them to work with any degree of accuracy a high quality headshot.