The processing is balanced between CPU & GPU processes. The reason is that iSentry analyses images at pixel level, uncommon in the market, instead of running full renders of each image to process It. This adds an additional layer of intelligence that is supplemented by several business intelligence analytics and other alarm reporting features.ĭeep diving into the technology, a key component is data processing, which is done with very little computing power. This allows iSentry to search for objects associated with events of interest generated by our algorithms in any part of the scene within any given time frame. ISentry also incorporates a powerful forensic search engine. For example, a person walking as far as 700+m among trees and bushes. With the correct configuration, TREX can detect small and fast-moving targets, as well as targets at great distance. TREX has the capability to learn the scene, allowing it to focus on true targets and not on environmental factors that are common to the scene such as moving trees and water reflections. TREX mimics human vision where we pay more attention to sudden movements and pay less attention to regular movement. ISentry’s Threat Detection and Extraction (TREX) capability, on the other hand, is designed for surveillance of large and wide areas and perimeters and capturing moving objects. Depending on the complexity of the scenery, the self-learning engine starts gathering smart insights of what is usual after 48 – 72 hours of operation. ISentry’s unusual behaviour (UB) capability is designed to add instant visual insight to the vast amounts of footage generated by a large-scale CCTV deployment. As a consequence, iSentry empowers control room operators, allowing them to solely focus on those decisions at which humans excel. Interestingly, such use cases are often events of Interest undetected by operators of control rooms, whom, after watching several screens for hours, would normally get tired. ISentry is architected and conceived to identify existing and discover new use cases autonomously. But what to do with the unknown? How to handle scenarios in which we don’t know what to look for? Very often they focus on one specific situation applying predefined rules such as trespassing or a specific object identification. Most video analytics products are built compiling use cases. Once all that data is processed by a Rules Engine, an alert is either dismissed if not relevant, escalated as an alarm if important or sent to an operator in the control centre: fire, robbery, thrown objects, left objects, trespassing, perimeter Intrusion, direction violation, aggression…. iSentry’s Deep Learning engine then enriches the event, providing classification and contextual data on the objects in the scene. Using the latest Artificial Intelligence (AI) technology, iSentry learns on its own the normal behaviour seen by a security camera and any unusual variation in that image generates a trigger. The goal is to watch less and focus on the important events of interest, in real time. ISentry is transforming the way CCTV control rooms operate. Solving that issue in traditional ways is expensive, unreliable and unscalable, mostly leading to forensic reviews rather than active real time monitoring. Indeed, the main challenge of the traditional CCTV surveillance system remains the inability to cope with the overwhelming demand for increased monitoring.Ī proactive security posture that handles threats at speed is absolutely essential. We see that more and more cameras are added to CCTV networks, while no human operator can view all those cameras attentively. Most technology systems focus on providing as much information as possible without taking the human attention span into consideration. Those circumstances are not only applicable to public areas and heavily crowded places such as airports, city centres and shopping malls, but equally so to more private areas such as residential and business buildings, banks, campuses, schools, manufacturing and operational sites.Ī wealth of information is creating a poverty of attention. Many major incidents in recent history have been attributed to a “failure of imagination” something happening that nobody even thought of or could imagine happening. The recent pandemic has added an extra dimension to it. The world is becoming more and more complex resulting in an ever-changing risk environment. Risks and threats are becoming more and more unpredictable. Artificial Intelligence-based video analytics is developing the way that CCTV control rooms operate, reports iSentry.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |