Page 16 - Security Today, September/October 2023

“With further training, a model could even learn to recognize and classify specific vehicle models from a chosen manufacturer.” classifying the differences between state license plates across the country – the analytics would likely reside on a server or in the cloud where it could efficiently compare a nationwide aggregate of motor vehicle data. Or for facial recognition analytics, the camera could capture an image of the face, but the image would likely need to be processed on a local or cloud server housing a large data set of facial images for comparison and ultimate identification. INTEGRATING SENSORS AUGMENTS SCENE INTELLIGENCE While advanced analytics can greatly enhance situational aware- ness, integrating intelligent video cameras with other sensor tech- nologies can take that scene intelligence to the next level. For in- stance, radar can provide another layer of context to the visual data such as the distance and speed of the person or object ap- proaching or departing the scene. It can provide early detection of an event and direct a camera to automatically track the person or object. Audio detection devices add acoustic intelligence such as the ability to recognize, categorize and alert to the sound of weapon fire, breaking glass, or an aggres- sive tone in voices. Like radar, audio detection can be used to direct cameras to the location of the sound to visually verify the event. To achieve that interoperability, however, the physical security ecosystem needs to be built on an open standards platform and support standardized interfaces between devices and analytics. Manufacturers of security equipment usually provide open Ap- plication Program Interfaces (APIs) and Software Development Kits (SDKs) to enable these multiple data types to communicate with each other and exchange information. AUTOMATING ALERTS AND RESPONSES BASED ON METADATA The ability to integrate scene intelligence from multiple devices and analytics not only helps to minimize false alarms it also gen- erates a wealth of metadata that the analytics can use to trigger timely alerts and activate specific responses. The decision tree for action can be programmed quite granu- larly based on the data the analytics are designed to detect and classify. The simplest action might be to send an event alert mes- sage to security operators or officers on patrol. Or, depending on the decision tree, place a 911 call to local responders via an inte- grated VoIP phone system. But the automated response could also involve triggering an action by other connected technologies in the ecosystem. For instance, as mentioned earlier, a radar sensor could send a geolocation alert to a camera to track an intruder’s movements. A fence guard analytic could trigger floodlights or a siren if it de- tects a person or vehicle attempting to enter a restricted area late at night. An audio analytic could initiate an automatic lockdown of all doors when it detects the sound of gunfire. Or, an object ana- lytic could trigger a network speaker to broadcast a pre-recorded message to move a vehicle detected blocking an emergency exit. Metadata also plays a key role in facilitating searches through live and archived video. In addition to basic timelines, the exten- sive data being captured by intelligent video makes it possible to specify extremely granular parameters such as a person wearing a blue baseball cap, a green shirt and white shorts, traveling right to left across the field of view. With the advent of natural language queries, these types of searches have become much easier to con- duct and yield faster, on-point results. APPLYING SCENE INTELLIGENCE TO UNIQUE APPLICATIONS What many companies are beginning to realize is that combining intelligent video with other intelligent sensors can serve purposes beyond physical security. In fact, just about any industry – from manufacturing to retail to aviation and education – could glean benefits from more comprehensive scene intelligence. For instance, video analytics could be linked with thermal camera analytics and industrial temperature gauges to detect and alert on combustible materials or overheating equipment on a factory floor. Video analytics linked with vape detectors can help schools detect and identify students who are vaping on campus. Hospitals can tie video analytics to access control technology to verify who is accessing the pharmaceuticals in a drug cabinet and trigger an alarm if there’s an identity discrepancy. Weather stations can use video analytics in conjunction with anemometers to track the speed and path of tornados and trigger emergency broadcasts through network speakers to communities in their path. The flexibility of an open development environment makes it easier for developers to create, train and integrate advanced deep learning modules into the ecosystem to meet customers’ unique needs. With a wealth of manufacturer embedded development tools and third-party programming toolkits at their disposal, they can build their analytics from the ground up or use open- sourced libraries of deep learning modules as building blocks for their customers’ specialized applications. INFLUENCING FUTURE DIRECTION While the fusion of advanced video analytics and other sensors can provide exceptional scene intelligence for real-time alerts and action, the wealth of metadata can also help an organization see where they have been and where they are going. The metadata generated by all these devices can be used to measure compliance or the progress of operations optimization, or any other activity along a timeline. And gaining a handle on where they’ve been and where they are now can help institutions better decide where they should be going. Robert Muehlbauer is a senior manager of Business Development for Axis Communications. 16 SEPTEMBER/OCTOBER 2023 | SECURITY TODAY INTELLIGENT VIDEO