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Assisting operators

Watching cameras continuously is just not an effective use of manpower.

Video systems generate a vast volume of picture data, but it is not economically sensible for people to review and evaluate all of it. Usually on sites where many video cameras are used, they have to be monitored by just a few operators. Watching all cameras continuously is just not an effective use of manpower because nobody can give their full attention to lots of images at once and operators inevitably tire quickly. Soon, lapses in concentration mean important details are overlooked and the whole security concept is significantly undermined. Though there may be a semblance of security, the reality is different.

To rectify this, relevant information needs to be filtered out of the mass. And thankfully, analysis algorithms are able to continuously examine as much picture content as you like and to automatically draw the operator’s attention to critical situations. Algorithms and processors do not get tired and they work automatically in the background 24 hours a day, seven days a week – just like a perfect assistant.

Clearly, any system must not generate many alarms without good reason, or operators will cease to treat them as critical situations and will just cancel the alarms without checking them out properly. Then the result is just the same as without video analysis — the operators lose concentration, real alarm situations are easily overlooked and the system is only actually providing a superficial impression of security.

Defining the task

So if video analysis is to be used efficiently and reliably, it is vital to have a clear definition of a so-called critical situation and a perfectly matched algorithm.

In the video system market in recent years, many marketing campaigns have featured sweeping statements about the capabilities of so-called intelligent video analysis. As a result, many users expect analysis algorithms to have human intelligence. They expect that if they themselves can see that it is a person crawling there and not a dog on all fours, then surely so must an algorithm which is supposed to be able to differentiate between humans and animals. If only it were that easy!

In fact, most algorithms use relatively basic criteria to differentiate, for example, vehicles and people from other moving objects and from each other.

But in order to make this logical deduction, the algorithm also has to have been told how big a person would be in the image when he is in the foreground and how big he would be in the background. This is done during set up when a service engineer measures the scene to determine how wide the foreground and background are. An operator who observes the scene does not need details like this to be defined explicitly. He knows simply from the context.

Classification algorithms like this are perfectly adequate for some applications. However, in situations where it is likely that people may not approach the site in an upright position (precisely because they do not want to be discovered), then this type of algorithm is completely unsuitable and a security risk.

Nowadays there is a large choice of algorithms which can undertake a wide variety of tasks. To provide really reliable assistance, the chosen algorithm needs to be suited to its particular task and must be set up perfectly. The more complex its computing process, and the faster its response time, then the greater the computing capacity it will require.

Centralised or decentralised

Many camera manufacturers already offer algorithms built into their cameras. These support decentralised video analysis conducted out at the edge of the video system rather than in the main server. This arrangement has the great advantage that it is uncompressed image data which is analysed – the perfect raw material for reliable analysis. This ‘analysis at the edge’ also saves bandwidth and reduces demands on computing capacity in the central computer.

When IP cameras are used and their images analysed centrally, then all the data is compressed first before being fed into the network and sent to the server which decompresses and analyses it. Unfortunately, this can result in analysis performance being impaired by compression artifacts. And it also inevitably means that the network is loaded with picture data which does not contain any important information and whose decompression makes additional demands on central server computing power.

But centralised analysis also has its advantages. It offers more flexibility in relation to the algorithms employed – often essential for specialist tasks; and it demands less computing power in the cameras – which may mean cheaper cameras can be used.

Both?

Often the best solution is a combination of different architecture types. You might want to use pre-analysis in the camera first to look for movement, then send selected footage to the central evaluation computer which then examines these pictures using a specialised or more complex algorithm. This second process filters out irrelevant motion and reports relevant situations reliably. Assuming that there is not constant movement in all of the camera scenes, then several cameras can share the network bandwidth and the computing capacity of the central server.

Video analysis is a sensible addition to modern video security systems particularly where it relieves the operator by providing efficient assistance.

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