Real World Opportunities

Historically, there were two things you could do with surveillance video—watch it or record it to watch later. Video analytics changed all that. Video content can now be analyzed using various computer algorithms to identify specific user-defined content and to trigger an alarm or response automatically. The range of video analytics functionality can include identification, behavioral analysis and situational awareness.

• By Bill Taylor
• Dec 01, 2011

With smart functions incorporated into today’s network video cameras, images can be analyzed before they leave the camera, which can then trigger an alarm or relay other data to enable a system-wide response. Alternatively, high-quality, higher-resolution images supplied by a camera can travel along a networked system and then be subjected to even more sophisticated video analytics at the server level.

In either case, the resulting system provides functional advantages that far exceed the view-or-record scenario that historically limited the effectiveness of video systems to the attention span of the operator.

In the last decade or so, while some in the surveillance industry were only beginning to embrace smart video, the technologies surrounding video analytics have continued to evolve and mature. What was once often dismissed as a technology that was difficult to install and plagued by false alarms has now become a useful, mainstream tool in the IP video system. Making the transformation possible are better cameras, more computational power— both in the camera and at the server level—and greater ease of use. Video analytics today are more robust, more dependable and more easily incorporated into the IP network environment.

Smarter Cameras = Better Images

The old computer acronym GIGO—garbage in, garbage out— also applies to video analytics. The quality of the image being analyzed has a critical impact on the effectiveness of the system. Fortunately, the images now being supplied by new, smart network cameras are more detailed and better than ever. Megapixel images are providing greater resolution, which translates into more-detailed images for analysis.

Enhanced image processing also helps overcome lighting challenges to produce usable images in a variety of real-world situations. Cameras can now provide a clear view of an entrance where complex lighting combines dark and light areas, and highresolution images can render an automotive license number perfectly legible.

One challenge is outdoor scenes, which are especially difficult due to lighting and seasonal changes. As edge devices in a video analytics solution, cameras can optimize the video image for better picture detail. For example, image processing can expand the dynamic range of an image to overcome lighting challenges that undermine the effectiveness of video analytics. In some applications, it can be difficult to identify the details of dark images. However, with video captured by smarter high-definition cameras, it is easier to identify the face of a person, to see what is being stolen, or to read a license plate.

The bottom line is, a better image equals better analytics. Improved video quality also extends to Face Wide Dynamic Range, specifically designed to enhance the details of a face for better identification and analysis.

In-camera video motion detection (VMD) functionality is now more sophisticated than ever, offering multiple programmable detection areas, multi-level sensitivity adjustments and multistep detection sizes. Smart cameras also offer a “privacy zone” function to mask private areas, such as house windows and entrances/ exits. Intelligent resolution can prevent deterioration of an image during digital zooming; and variable resolution technology makes it possible for a less-important part of an image—such as the sky—to be coded at a lower resolution to save data file size.

Features such as wide dynamic range, adaptive black stretch, and 3D digital noise reduction make images clearer and more detailed; progressive scan provides images with no motion blur or tearing; and H.264 full-frame-rate video can be recorded at the camera level using an SD/SDH memory card.

All of these expanded features enhance the opportunities to use video analytics. Integrators must be attentive to the angle of the camera, camera positioning and lighting. Camera setup can be simplified by use of auto back-focus technology to provide optimal image quality and focus with a single push of a button.

On-board Analytics

Embedding technologies into edge devices—as opposed to using centralized platforms—makes it easier for integrators to install and set-up video analytics solutions. Intelligent network cameras now offer face recognition, advanced motion detection and auto tracking, among other functions.

Video analytics inside cameras on the edge of the network can identify objects left behind or track customer traffic patterns or count crowds. These functions at the camera level can be integrated into systems that provide additional functionality.

More on-board intelligence empowers additional and more effective video analytics. However, the centralized approach has its own advantages. Chief among these is a collective database, along with system-wide real-time control and management of functionality and alarm response. A combination of centralized intelligence and video analytics at the edge works well. Intelligence inside the camera helps to minimize the system’s computational load and the amount of data that travels across the network, which makes for better use of network infrastructure, while core capabilities are still centrally managed.

System Considerations

Some integrators may have been intimidated by the complexity of programming video analytics systems, which often provide many configuration choices. Integrators have tended to avoid the extra expense and time involved in completing these more challenging installations.

Early installations of video analytics systems were beset by system crashes and other problems—and the time lost in figuring out what went wrong. Integrators had to be precise in installing these systems because one error could undermine the whole system. Also, installation typically involved many separate components from various suppliers. Integrators had to be mindful of factors such as computer requirements, software updates and security patches.

Currently, the move toward smarter edge devices has made it easier to install video analytics. These systems require less configuration because edge devices—cameras—have on-board intelligence. Because analytics are already installed and pre-programmed into the device, there are fewer trouble-shooting steps, and the systems are much easier to configure. In short, intelligent video cameras provide an entry-level opportunity for integrators to get comfortable with analytics without having to deal with higher system complexity.

More intelligence in the edge device can help pre-select and filter what video is shared across the network. Using the network infrastructure to view only selected video can help to minimize bandwidth and storage requirements, which can be a concern with systems that incorporate megapixel cameras.

Newer network cameras also can detect faces automatically, even in high-contrast lighting situations, and even if there are multiple people in a frame. The use of Face Wide Dynamic Range functionality ensures a clear image of a face, and a facedetection function detects the face’s position. In some configurations, the camera detects the face and sends metadata to an NVR, where the metadata is analyzed and compared to an existing database. Upon a positive face match, alarm notification can be sent, and the image can be displayed. An NVR with a real-time face-matching function works in tandem with a smart camera, comparing any faces detected by the camera against a database of previously registered faces.

Higher resolution works with in-camera intelligence to ensure more detailed metadata is available for face-matching capabilities. In-camera analytics also extend to identifying the gender of a captured image and other variables.

A Range of Applications

From retail to transportation, education to homeland security, there are many diverse applications for smart camera technologies. In the retail environment, smart cameras can be used to detect a habitual shoplifter among several customers entering a store at once. Another application might be to identify and alert management when a VIP customer enters a business. Identifying customer traffic patterns, sounding an alert when a check-out line is too long or ensuring that customer service practices conform to a standard are all applications that can benefit from smarter video cameras. Beyond the security and loss prevention benefits, the technology provides quantitative improvements to business operations than can impact the bottom line.

In homeland security or large stadium applications, smart cameras as edge devices can enable video analytics applications such as identifying objects removed or objects left behind. At schools and colleges, motion-triggered cameras can identify campus activity after-hours. In critical infrastructure environments, video analytics could specify virtual trip wires and provide an alarm when someone crosses the designated border. In transportation/ gas station applications, high-resolution images make it possible to capture a license plate number, whether viewing archived video or using license plate recognition (LPR) software. In public areas and town center applications, clear color images are available 24-hours a day through the fusion of high sensitivity and adaptive digital noise reduction with auto back-focus.

In a banking or financial application, cameras can provide a clear view of an entrance where the lighting situation is complex due to a mixture of dark and light areas. A megapixel image, combined with image processing to maximize the details despite variable lighting, enables precise identification and efficient bank operations. Face recognition can provide an alert when a known criminal enters the bank.

In short, the possible applications of video analytics are just now being realized. Focusing on a broader array of benefits beyond security is an opportunity for suppliers to help customers enhance cost justification strategies. Video and data can be integrated with other applications such as retail systems, human resources, process management and access control systems.

Putting “Smarter” to Work

Objects all around us are becoming smarter every day as computer intelligence shows up almost everywhere in our daily lives. Smarter machines are made possible by microchips and highdensity microstructure processing technology, and we see daily examples of how embedded intelligence can transform the functionality of everyday things.

In the case of video surveillance, the ability to use computer algorithms to analyze and interpret the content of video presents a new level of system functionality, provided the capability is strategically employed in the broader system environment. Once considered systems in themselves, video analytics can today best be thought of as a capability that can be incorporated into an IP-based networked system. Video analytics operating as part of a larger system provide a host of benefits to boost functionality and to overcome the limitations of a system’s human component. Video analytics can alert operators to actual incidents, eliminating or reducing the need for individuals to watch hours of video just waiting for something to happen. Using analytics, a system is more effective and provides greater security.

However, video analytics is a tool, and as such does not apply to every situation. Thoughtful and selective application of this powerful technology will likely be the largest factor to ensure its continued and growing success in the future.

For the video surveillance industry, embedded intelligence opens a host of possibilities centering on system functionalities and interoperability. When you factor in advancements in highdefinition imaging, the future of networked video surveillance is even brighter. Better images and a heightened capability to analyze the content of those images provide a powerful combination that can transform how video systems are used. The two technologies work together in ways that are greater than the sum of the parts, and surveillance system integrators and users reap the benefits of the resulting improvements in system functionality and performance.

This article originally appeared in the December 2011 issue of Security Today.