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CRIME PREVENTION & PASSENGER SAFETY


Video analytics: specifi c by design


Systems using video analytics are often highly specialised with above-average demands for accuracy and availability. Since they are generally used to detect particular items, analytics applications require highly customised installations and confi gurations. This article, by Kate Huber of Siqura B.V., explores the technology behind video analytics and explains why these advanced software programs are specifi c by design.


Understanding the ins and outs of analytics


W


hile it is amazing what technology can do, the nitty-gritty of it does not work


like the latest sci-fi movie. Video analytics work in a highly organised and structured manner. It is therefore important to have a basic understanding of how analytics work to better grasp how it is applied in various situations.


In contrast to video motion detection (VMD), which compares image frames to detect motion in the camera scene, video analytics examine the actual video material by: • Differentiating between the general camera scene and objects passing through it; • Using specifi ed parameters known as ‘metadata’ to detect objects – metadata includes things like the speed and relative position of the object within the camera scene; • Classifying and evaluating the detected objects based on predefi ned sets of rules, including perimeter detection, counting, minimum object lifespan, and aspect ratio; a set of rules can roughly categorize objects, for example, as humans, animals, or vehicles, or


situation. A large part of creating an effective system depends on where analytics algorithms reside on a particular network.


Getting the best of both worlds Managing a metro


Video analytics programs are relatively fl exible in where they can be deployed. They can run: • In stand-alone devices


• On centralised computers and servers • In a distributed setup


There are certainly pros and cons to every confi guration. A centralised setup makes it possible to use different types of devices (e.g., IP or analog) and to combine information from the various devices. However, it requires a lot of processing power (CPU) and the analytics algorithms work on compressed video rather than original images.


Embedding analytics in edge devices (e.g., cameras and codecs) enables algorithms to scan raw video signals and allows a relatively simple but intelligent system to be set up anywhere. Yet, stand-alone edge devices are not capable of tracking objects or analysing recorded


Delayed trains are not an option for the metro system of a world-renowned mega metropolis. That’s why authorities installed an intelligent IP video surveillance system.


The system allows authorities to defi ne a number of incidents that could hinder metro schedules, such as an object on the tracks or a stopped train. Analytics algorithms continually monitor camera scenes and trigger an alarm when certain parameters are met.


One incident that authorities want to be sure to avoid is pedestrians in a metro tunnel. However, the algorithms initially gave a number of false alarms due to the extreme difference in the camera scene between when a train was and was not passing through the tunnel. Authorities equipped the tunnels with sensors to shut down the analytics system while trains passed. When the camera’s fi eld of view returned to the preconfi gured ‘normal’ scene, the analytics would start up again.


This setup did the trick and the operators can now profi ciently ensure that the metro runs safely and on time.


Undermining universal applicability Above: Analytic confi guration; and an analytic event.


determine the density of objects in a specifi c area. More advanced systems can track objects, accumulate (behavioral) statistics, and read licence plates or container labels.


The nature of these applications allows very little margin for imprecision or inaccuracies. Analytics deployments are therefore only successful if they are designed for a specifi c


material. Moreover, they have relatively limited processing power. Therefore, many choose for a distributed analytics deployment to optimise the allocation of resources. In distributed networks, edge devices take on some of the number crunching while the central server provides ample amounts of CPU and memory power for demanding tasks. Ultimately, a hybrid setup reaps the best of both worlds and


Video analytics offer intelligent solutions to detect, classify, and track almost anything. Accurate parameters are essential to the success of an analytics application and every installation must be designed and fi ne tuned for the specifi c task at hand. Ultimately, this enhances the overall effectiveness of surveillance installations and allows operators to focus on dealing with rather than discovering incidents.


FOR MORE INFORMATION


T: +31 182 592 215 W: www.tkhsecurity.com


rail technology magazine Aug/Sep 12 | 109


facilitates the customisation of the solution. The following scenario gives a better idea of how analytics might be deployed in the real world.


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