The video analytics industry is still shaking off the reputational damage it sustained as a result of previously promising too much and delivering too little in the past. For years, the reliability of video analytics has been extremely variable, with vendors struggling to develop algorithms that could function in complex scenes. The industry has come a long way in recent years, and the capabilities of more conventional video analytics have steadily increased. However, deep-learning analytics are poised to revolutionise the industry and facilitate a leap in the capabilities of video analytics. In the last couple of years, there has been a marked increase in research and development in deep-learning neural networks, proving their capabilities, generating considerable excitement and putting them within reach of a much wider user group.
Delivering better accuracy and reliability
Deep learning appears to be able to offer a level of accuracy and reliability in object and behaviour classification that not only enables video analytics finally to deliver on some of the lofty – but as yet unrealised – claims made in the past, but pushes capabilities far beyond them. Broadly speaking, there are two main areas in which deep learning analytics offer great benefits over the technology that has preceded it. They are:
A long-held complaint levelled against traditional analytics products was that their algorithms were unable to distinguish between objects and behaviours that a human being would have no problem classifying. This deficiency in computer vision algorithms results either in missed security breaches or false alarms. The ability of deep learning algorithms to view a scene intuitively, as a human viewer would, means that detection accuracy increases dramatically while false alarm rates fall. Neural networks allow a computer to apply a series of assessments to a given situation. This is an important development for the video analytics industry. Although some end-users may not need an analytics solution that is 100 percent accurate 100 percent of the time, many use cases require the security system to be as close to infallible as possible. Users in the critical infrastructure sector, for instance, cannot afford to miss a breach in security, and can spend a large amount of money investigating false alarms. Deep learning algorithms have shown they can learn to achieve 99.9 percent accuracy in certain tasks, where conventional systems would struggle to achieve 95 percent. In many security use cases, these few percentage points make all the difference.
Not only has deep learning demonstrated its capacity to increase radically the effectiveness of a computer to reliably classify objects and behaviour, it also is making possible the processing and analysis of increasing volumes of video footage in a fraction of the time required of earlier analytics. Companies such as Avigilon, Qognify, and IronYun now are marketing analytics that leverage deep learning to turn vast amounts of video footage into usable information in a fraction of the time it would have taken in the past. Video processing software that allows users to interact with their surveillance footage using a Google-like interface and natural language search terms drastically reduces the time it takes to find relevant video footage in an archive that might store video from thousands of feeds.
Facial recognition is an area that has benefited greatly from deep learning architecture. Indeed, most facial recognition analytics on the market today feature some kind of deep learning. Not only does deep learning increase the accuracy of facial recognition sensors, it also enables faces to be identified in larger and more crowded scenes. In the wake of recent terrorist attacks in crowded locations, this capability could radically change the whole approach to security monitoring, allowing law enforcement to track suspects with far greater speed and efficiency.