Applied Technology Review : News

Using video analytics to add intelligence to surveillance cameras may open new possibilities, improve security, and increase operational efficiency.  Receiving a security breach warning is the first step in recognizing suspicious behavior, but the cameras also collect additional data that might assist users in taking remedial action. The latest Positional Camera Tracking feature within Security Center enables users to identify and monitor intruders on an interactive map by leveraging current cameras combined with video analytics. Once an alarm has been triggered, users may locate the intruder's exact position and stream their images to get a more complete and action-oriented view of the activities. Here are the positive benefits of transforming your camera into radar: Use any IP camera to get the benefits of radar Perimeter intrusion detection sensors, including radars and lidars, are often used to warn and identify the instant an activity crosses the perimeter of a barrier. Although this level of precision is pricey, radar sensors that provide geo-coordinates of an incursion increase an operator's visibility and accuracy of an occurrence. Instead, these same features may get added to the company's current cameras, which gets already installed in strategic areas. Users can follow targets on a map without investing in a new radar system by merging the existing camera feed with a GPS source. Acquire situational awareness to take remedial action as soon as possible. Even across numerous locations, each intrusion detection zone might be totally distinct. Based on the operating needs of the site, it's critical to be able to control incursions vs. false alarms as when they Unify multiple disconnected systems Using several intrusion detection sensors to defend the perimeter is normal practice at any sensitive facility. These many detecting sensors (radar, Lidar, video analytics, PIDS, and lasers) are often separate systems that do not interact with one another. However, users can see the broad picture by moving existing system capabilities to perimeter monitoring rather than focusing on many individual intrusion detection instances. With a single physical security platform, businesses can easily aggregate information from all of the existing sensors and control intruder monitoring on a map, all while keeping an eye on the target. Using video analytics to add intelligence to surveillance cameras may open new possibilities, improve security, and increase operational efficiency. ...Read more

Computer vision has evolved into a critical technology for replacing human supervision and monitoring in the last decade. In the past decade, computer vision has become a vital technology for various applications that replace human supervision and monitoring. This article presents a scientific summary of the most recent advancements in computer vision for video surveillance and AI security monitoring. The Cutting Edge of AI Video Surveillance Computer vision analyzes and interprets video data using various technologies. The fundamental objective of computer vision in surveillance and security business applications is to automate human supervision. The capacity to capture and digitize real-world scenarios offers new potential to detect threats more effectively and sooner, quantify risk, and conduct real-time security assessments. New Machine Learning, Edge Computing, Artificial Intelligence, and Internet of Things (IoT) technologies make AI vision far more potent, adaptable, and scalable, causing the number of computer vision applications to rise fast. Superior Artificial Intelligence for Computer Vision in Surveillance Recent advancements in deep learning and edge computing have made computer vision possible for the first time. Deep learning, a subset of machine learning, allows machines to learn from training data and apply the resulting algorithms to new data. Edge Computing is the practice of relocating computing workloads from the cloud to the network's border close to the data source (camera). Thus, edge computing addresses the difficulties associated with linked cameras and gadgets, such as network congestion, continual connectivity, latency, resilience, privacy, and data management. Modern computer vision systems use Edge computing to process video without transferring video data to the cloud or another storage device. The combination of machine learning on the device and edge computing is also known as Edge AI or Edge Intelligence. In computer vision applications for surveillance and security, these new technologies are crucial in allowing actual AI applications. In addition, Edge AI vision infrastructure enables substantial cost reductions in real-time, large-scale computer vision systems. Intelligent Surveillance Cameras Using the extensive deployment of security cameras in public areas, AI video analysis and scene interpretation with computer vision have become indispensable components of surveillance systems. Compared to other data sources, such as mobile location, GPS, radar signals, etc., visual data from camera streams provide a wealth of information. Large-scale video analytics systems can collect statistical data regarding road traffic, public areas, buildings, and private properties. Modern AI vision software permits the video feed from almost any network camera. A single-edge device can process the video feeds from numerous cameras depending on its hardware architecture. Powerful edge servers may analyze anywhere from dozens to tens of thousands of cameras. Some IP camera manufacturers and turnkey point solutions offer on-camera intelligence in which the computing processor is included within the camera. Enterprise systems typically segregate AI computation from the camera for various reasons. First, firms must maintain vendor independence and the flexibility to negotiate. Then, businesses must avoid technology lock-in and ensure system extensibility and integration. In addition, cameras with built-in AI processing do not permit scaling up hardware resources if a business needs to expand features or improve AI performance. In addition, most companies operate video surveillance systems with various cameras of various brands, generations, and types (Sony, Panasonic, Axis, Hikvision, Dahua, Samsung, and so on). It would be too expensive to replace all cameras, and standardization would incur lock-in expenses. Additionally, the majority of camera devices are replaced every two years. AI Video Monitoring Systems Traditional video surveillance systems rely solely on human operators and their judgment and vigilance, and intelligent AI analysis provides human operators with highly rapid, objective, and consistent data. Depending on the application, the AI vision program detects and predicts traffic congestion, security concerns, accidents, and other irregularities. The software capabilities of a typical computer vision system include data input acquisition, image preprocessing, deep learning inference, output aggregation, communication, and visualization. Such a computer vision system can execute a single program or numerous apps that each handle distinct challenges (anomaly detection, pose detection, object detection, etc.). ...Read more

Since GPR technology has become more affordable and easier to use, it has changed how civil construction engineers, government councils, and project managers involved in large-scale projects work around underground services today. Ground Penetrating Radar (GPR) locates utilities using electromagnetic radio waves pulsed into the ground. The energy waves then reflect off underground services and objects buried in the subsurface. Utility engineers use GPR to investigate underground utilities in many different ways, and it is important to detect underground pipes and cables. Construction units can obtain data on what lies below the ground that the right people can interpret to determine the location of underground services using ground penetrating radar. Using Ground Penetrating Radar can reveal issues with subsurface infrastructure that could otherwise go undetected. GPR offers a unique advantage over other techniques, allowing workers to detect subsurface issues without digging or causing any disruption accurately. Quick to set up: GPR does not require connections to utilities. It is easy to set up a ground-penetrating radar since it is non-invasive. It is not necessary to excavate to determine what lies beneath the surface. It is also possible to penetrate the concrete, soil, and even water with ground-penetrating radar. Due to its versatility, GPR is essential for various purposes, including search and rescue, surveying, and archaeology. Does not need a connection to the utility: It detects underground utilities without requiring a connection. It can identify utilities because it uses electromagnetic waves to penetrate the ground. A machine can identify the utility locations by bouncing the waves off the ground and back to the machine. The system does not require any other utilities to be connected to it. Overcomes limitations of electromagnetic locating methods: A major advantage of GPR over electromagnetic (EM) location is that GPR provides better imaging for all types of utility lines, unlike EM. There is no difference between metallic and nonmetallic lines when using GPR since it detects ground disturbances rather than lines. GPR-only surveys will produce different final drawings from EML-only surveys, with the GPR detecting underground features and recovering more beneath-surface information but unable to identify the various utilities. Although EM surveys identify services and critical information from site reconnaissance, the drawing will not include the additional details that GPR recovers, nor will it be able to detect services that are nonmetallic. ...Read more