Dr. Boghos Boghossian
Analytics at the edge provide the ability to process what is happening in a field of view and discern if a relevant alert is triggered There are multiple benefits to using video analytics at the edge (i.e., near or inside the camera). For one thing, analytics at the edge provides the ability to process what is happening in a field of view and discern if a relevant alert is triggered. This can be faster and less expensive than the original video analytics model of using a separate dedicated server. However, there isn’t one right solution, as a video analytics' complexity and a camera’s processing power are not always aligned. Some analytics can begin the analysis at the camera and also utilize a server to balance the workload. Others may be best used in server-only models. Speed of alert is of importance, as results that are not urgent may not dictate a powerful camera. Another variable is whether the system needs actual video of an event or just information (metadata) from that video. When recorded video is not required at a server, intelligent cameras at the edge help lessen the required bandwidth, says Brian Lane, director of marketing, 3VR. He says intelligent cameras and the cloud go hand-in-hand. For example, only metadata is needed when counting people; therefore, intelligent cameras can do all the processing in the camera, and only the metadata is sent to the cloud. For security, only a low-bandwidth stream is sent to the cloud, while the high-resolution video is stored at the camera. When video is required, the edge advantage becomes far less, since the video must reach the server to be recorded, adds Lane. Having analytics such as face and demographics at the server level keeps the cost of the cameras low since the processor on the server does most of the work. Processing power on servers is far cheaper than having a robust processor in each camera. Analytics that require a lot of processing power greatly increase the cost of the cameras, since they must have a robust processor. When the processing takes place at the server level, the customer can keep overall costs down by using far cheaper cameras and using a centralized server-based system. Edge-based analytic cameras offer a host of benefits to facilities that need to monitor large perimeters, complex campus environments or geographically dispersed open spaces Sometimes, a combination is optimal. For example, Agent Vi has a patented approach that enables analytics processing both at the server and distributed to the edge. The Agent VI system operates on a server between the camera and the video management system (VMS), analyzing video streams and providing output of that analysis. A software module called “Vi Agent” runs inside video encoders and cameras at the edge (including brands such as Axis, Samsung, Hikvision, and Vivotek). The Agent Vi software completes “preprocessing” at the edge and sends information to the server, which completes the process and provides the output. Unlike strictly edge-based analytics, the approach is not limited by processing power and memory in the camera. Compared to server-only installations, the system is more scalable (by a factor of 10 to 20 compared to server-based systems), says Zvika Ashani, chief technology officer (CTO), Agent Video Intelligence (Agent Vi). The Vi Agent and server are the same for various verticals; various functionalities are activated per user based on license keys, with various licensing at different price points. Ashani notes a trend in the market of camera vendors turning their cameras into open platforms to allow software vendors to load analytics (and other applications) onto the cameras. Previously, software vendors had to work closely with camera vendors, even creating special software versions. “Today, the cameras are not yet at the level of an iPhone or Android [platform], but they are much more open and there is greater variety in terms of applications you can load,” he says. Ipsotek has always seen edge-based analytics as an interesting alternative to traditional server-based (centralized) solutions. Edge deployment lends itself to a distributed solution where infrastructure is not available, hence where transmitting video of high quality to a centralized server is not an option. Transport (road/rail) has been a major beneficiary of edge-based analytics technology, says Dr. Boghos Boghossian, CTO, Ipsotek. The lack of infrastructure results in a need for a more complex management of rules and possibly more challenging environmental aspects. In order to operate advanced video analytics solutions at the edge, a suitable hardware platform should be provided with enough processing power. However, often at the edge, the system must be rugged and should operate at high temperature extremes; consequently, the availability of such a hardware platform is less likely. There isn’t one right solution,as a video analytic’s complexityand a camera’s processing powerare not always aligned “Because of these issues, most manufacturers have opted to offer only basic analytics solutions at the edge,” says Boghossian. “Ipsotek took a different route, and through the use of digital signal processing technology, has managed to move its technology to the edge with no compromise to performance, feature list or robustness.” Ipsotek has been offering cloud-based systems to a number of large customers for a few years. The interesting correlation is the larger adoption of cloud-based solutions in projects based on edge analytics due to the lack of infrastructure and therefore reverting to cloud storage for data management. This trend may soon be overtaken by cloud-based video analytics, which is waiting for sufficient affordable bandwidth to stream video to the cloud at the required speed and quality. Edge-based analytics run on raw video data as opposed to encoded video on the server, allowing the analytics to gather more sensitive and accurate data, says Maor Mishkin, director, Video Analytics Product Champion, DVTEL. In addition, it allows the analytics to control the sensor and enable optimized video input for the analytic engine. Edge-based analytic cameras offer a host of benefits to facilities that need to monitor large perimeters, complex campus environments or geographically dispersed open spaces. Edge-based analytic devices do not rely on servers or third-party software. This reduces the network bandwidth requirements while maintaining performance at the highest level. In addition, when technology developers offer a complete solution that ties in edge analytics and video management, users benefit from a single, tightly integrated solution, which means there is less opportunity for failure, Mishkin says.
The better the sensors, the better the analytics Garbage in, garbage out. The familiar cliché is just as applicable to the area of video analytics as any other field of computing. You simply must have a high-quality image in order to achieve a high-functioning analytics system. The good news is that video cameras, which are the sensors in video analytics systems, are providing images that are better than ever, offering higher quality – and more data – for use by video analytics. For analytics that require a higher resolution to achieve superior results, megapixel cameras provide video that allows for better face recognition, clearer license plate numbers, reliable age and gender of customers, and other uses. These help prevent false positives and increase reliability in forensic searches, says Brian Lane, director of marketing, 3VR. When Ipsotek considers a video analytics-based solution, 50 percent of that solution is reliant on the selection of the appropriate sensor (camera). With the emerging technologies of thermal, megapixel and advances in camera processing, this half of the solution is more readily achieved, says Dr. Boghos Boghossian, CTO, Ipsotek. In some areas like face recognition, the illumination of the face in challenging environmental conditions is key to the success of the solution. Therefore, Ipsotek has been evaluating cutting edge camera technology provided by Ipsotek’s technology partners to assist consultants and solution partners to design successful solutions for every growing video analytics market. The better the sensors, the better the analytics, agrees Dr. Rustom Kanga, CEO of iOmniscient, and lower costs of thermal cameras make them a good choice. However, cameras that provide higher-resolution images require more computing power, bandwidth, and storage, which complicates their use with analytics. In general, the resolution is downgraded to the least resolution possible to detect the activity the analytics system is looking for. For analytics that require a higher resolution to achieve superior results, megapixel cameras provide video that allows for better face recognition, clearer licence plate numbers, reliable age and gender of customers, and other uses iOmniscient has a new technology called IQ Hawk that “pulls out of the image what is important,” says Kanga. It accesses higher resolution only for areas of interest in the photo – such as using higher resolution of a face or licenseplate viewed from a distance to enable facial or license plate recognition. The rest of the image is used at lower resolution. If there are three people in a video frame, IQ Hawk presents all three faces in high-res to enable identification. “With IQ Hawk, we can dynamically look at an image at high and low resolution, based on what’s important,” says Kanga. In terms of using higher-resolution cameras with analytics, Zvika Ashani, chief technology officer (CTO), Agent Video Intelligence (Agent Vi), says it is important to consider the “lowest common denominator” in terms of usable resolution. For example, a megapixel camera might have a clearer image in good sunlight; but at nighttime, the image will suffer, and could be worse than a low-resolution image. “More pixels don’t mean more detection quality,” he says. “The more pixels you have, the more processing power you need inside the camera.” Therefore, high-resolution images may even be “downscaled” to a lower resolution for analysis to minimize the amount of data to be managed. Higher resolution can also introduce additional noise in many cases. Some higher-resolution cameras have video analytics built in. DVTEL’s new ioimage HD Analytic IP cameras provide HD broadcast-quality IP video coupled with built-in military-grade analytics. These high-resolution, low-bandwidth cameras, available in both HD 1080p and 720p, are optimized for outdoor conditions and available with predictable storage. The cameras have enhanced low-light and no-light capabilities, high sensitivity, and true wide dynamic range. A new analytics feature provides a reduced false alarm rate for people standing upright, which benefits applications that don’t need sophisticated detection of camouflaged or crawling intruders. ioimage analytics now have improved detection distance, which allows for fewer cameras needed to cover the same area.
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