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Vision-based AI boosts surveillance applications

Posted: 17 Jun 2014 ?? ?Print Version ?Bookmark and Share

Keywords:automated surveillance? artificial intelligence? embedded vision? DSP? SOCs?

The term "embedded vision" refers to the use of computer vision in embedded systems, mobile devices, PCs, and the cloud. Historically, image analysis techniques have only been implemented in complex and expensive, therefore niche, surveillance systems. However, the previously mentioned cost, performance and power consumption advances are now paving the way for the proliferation of embedded vision into diverse surveillance and other applications.

Automated surveillance capabilities
In recent years, digital equipment has rapidly entered the surveillance industry, which was previously dominated by analogue cameras and tape recorders. Networked digital cameras, video recorders, and servers have not only improved in quality and utility, but they have also become more affordable. Vision processing has added artificial intelligence to surveillance networks, enabling "aware" systems that help protect property, manage the flow of traffic, and even improve operational efficiency in retail stores. In fact, vision processing is helping to fundamentally change how the industry operates, allowing it to deploy people and other resources more intelligently while expanding and enhancing situational awareness. At the heart of these capabilities are vision algorithms and applications, commonly referred to as video analytics, which vary broadly in definition, sophistication, and implementation (figure 1).

Figure 1: Video analytics is a broad application category referencing numerous image analysis functions, varying in definition, sophistication, and implementation.

Motion detection, as its name implies, allows surveillance equipment to automatically signal an alert when frame-to-frame video changes are noted. As one of the most useful automated surveillance capabilities, motion detection is widely available, even in entry-level digital cameras and video recorders. A historically popular technique to detect motion relies on codecs' motion vectors, a byproduct of the motion estimation employed by video compression standards such as MPEG-2 and H.264.

Because these standards are frequently hardware-accelerated, scene change detection using motion vectors can be efficiently implemented even on modest IP camera processors, needing no additional computing power. However, this technique is susceptible to generating false alarms, because motion vector changes do not always coincide with motion from objects of interest. It can be difficult to impossible, using only the motion vector technique, to ignore unimportant changes such as trees moving in the wind or casting shifting shadows, or to adapt to changing lighting conditions.

Such "false positives" have contributed to the perception that motion detection algorithms are unreliable. To prevent vision systems from undermining their own utility, installers often insist on observing fewer than five false alarms per day. Nowadays, however, an increasing percentage of systems are adopting intelligent motion detection algorithms that apply adaptive background modelling along with other techniques to help identify objects with much higher accuracy levels, while ignoring meaningless motion artifacts.

While there are no universal industry standards regulating accuracy, systems using these more sophisticated methods regularly achieve detection precision approaching 90 per cent for typical surveillance scenes, even those with those adequate lighting and limited background clutter. Even under more challenging environmental conditions, such as poor or wildly fluctuating lighting, precipitation-induced substantial image degradation, or heavy camera vibration, accuracy can still be near 70 per cent. The more advanced 3-D cameras discussed later in this article can boost accuracy higher still.

The capacity to accurately detect motion has spawned several event-based applications, such as object counting and trip zone. As the name implies, 'counting' tallies the number of moving objects crossing a user-defined imaginary line, while 'tripping' flags an event each time an object moves from a defined zone to an adjacent zone. Other common applications include loitering, which identifies when objects linger too long, and object left-behind/removed,which searches for the appearance of unknown articles or the disappearance of designated items.

Robust artificial intelligence often requires layers of advanced vision know-how, from low-level imaging processing to high-level behavioural or domain models. As an example, consider a demanding application such as traffic and parking lot (car park?) monitoring, which maintains a record of vehicles passing through a scene. It is often necessary to first deploy image stabilisation and other compensation techniques to retard the effects of extreme environmental conditions such as dynamic lighting and weather. Compute-intensive pixel-level processing is also required to perform background modelling and foreground segmentation.

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