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Enhancing surveillance apps with vision-based AI

Posted: 22 Aug 2014     Print Version  Bookmark and Share

Keywords:automated surveillance  artificial intelligence  embedded vision  DSP  SOCs 

Some algorithms, such as those found in OpenCV (the Open Source Computer Vision Library), are cross-platform, while others, such as Texas Instruments' IMGLIB (the Image and Video Processing Library), VLIB (the Video Analytics and Vision Library) and VICP (the Video and Imaging Coprocessor Signal Processing Library), are vendor-proprietary.

Leveraging pre-existing code speeds time to market, and to the extent that it exploits on-chip vision acceleration resources. It can also produce much higher performance results than those attainable with generic software (figure 3).

Figure 3: Vision software libraries can speed a surveillance system's time to market as well as notably boost its frame rate and other attributes.

Historical trends and future forecasts
As previously mentioned, embedded vision processing is one of the key technologies responsible for evolving surveillance systems beyond their archaic CCTV (closed-circuit television) origins and into the modern realm of enhanced situational awareness and intelligent analytics. For most of the last century, surveillance required people, sometimes lots of them, to effectively patrol property and monitor screens and access controls.

In the 1990's, DSPs and image processing ASICs (application-specific integrated circuits) helped the surveillance industry capture image content in digital form using frame grabbers and video cards. Coinciding with the emergence of high-speed networks for distributing and archiving data at scales that had been impossible before, surveillance providers embraced computer vision technology as a means of helping manage and interpret the deluge of video content now being collected.

Initial vision applications such as motion detection sought to draw the attention of on-duty surveillance personnel, or to trigger recording for later forensic analysis. Early in-camera implementations were usually elementary, using simple DSP algorithms to detect gross changes in greyscale video, while those relying on PC servers for processing generally deployed more sophisticated detection and tracking algorithms.

Over the years, however, embedded vision applications have substantially narrowed the performance gap with servers, benefiting from more capable function-tailored processors. Each processor generation has integrated more potent discrete components, including multiple powerful general computing cores as well as dedicated image and vision accelerators.

As a result of these innovations, the modern portfolio of embedded vision capabilities is constantly expanding. And these expanded capabilities are appearing in an ever-wider assortment of cameras, featuring multi-megapixel CMOS sensors with wide dynamic range and/or thermal imagers, and designed for every imaginable installation requirement, including dome, bullet, hidden/concealed, vandal-proof, night vision, pan-tilt-zoom, low light, and wirelessly networked devices.

Installing vision-enabled cameras at the 'edge' has reduced the need for expensive centralized PCs and backend equipment, lowering the implementation cost sufficient to place these systems in reach of broader market segments, including retail, small business, and residential.

The future is bright for embedded vision systems. Sensors capable of discerning and recovering 3-D depth data, such as stereo vision, TOF (time-of-flight), and structured light technologies, are increasingly appearing in surveillance applications, promising significantly more reliable and detailed analytics.

3-D techniques can be extremely useful when classifying or modelling detected objects while ignoring shadows and illumination artifacts, addressing a problem that has long plagued conventional 2-D vision systems. In fact, systems leveraging 3-D information can deliver detection accuracies above 90 per cent, even for highly complex scenes, while maintaining a minimal false detection rate (figure 4).

Figure 4: 3-D cameras are effective in optimising detection accuracy, by enabling algorithms to filter out shadows and other traditional interference sources.

However, these 3-D technology advantages come with associated trade-offs that also must be considered. For example, stereo vision, which uses geometric "triangulation" to estimate scene depth, is a passive, low-power approach to depth recovery which is generally less expensive than other techniques and can be used at longer camera-to-object distances, at the trade-off of reduced accuracy (figure 5).

Figure 5: The stereo vision technique uses a pair of cameras, reminiscent of a human's left- (top) and right-eye perspectives (middle), to estimate the depths of various objects in a scene (bottom).


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