Just a decade ago, CCTV cameras were basic event recorders that humans entirely operated. Well, this concept is still relevant for small enterprises or homeowners. Nowadays, specialized professional vendors like CCTV Norway supplier CamHi offer hardware that can be used for any need.
Nevertheless, modern big-scale solutions are a whole new approach to camera utilization. Right now, cameras are used for any kind of recording, from classic security systems to traffic control. Modern cameras can even fly and analyze data via AI. As you may have imagined, this hardware is used everywhere it’s financially justified, especially in society-scale projects like smart cities.
CCTV Systems & AI
The modernization and management of modern cities rely on real-time data collected by hundreds of millions of IoT sensors installed worldwide. Although there are a lot of sensors, their number is growing exponentially. A camera might be one of these sensors that provides a rich array of information.
However, cameras generate a tremendous amount of data. This data needs to be analyzed in real-time, ideally as close to the source as possible, to minimize data connection requirements. This is where artificial intelligence comes into play. It can recognize individual objects in the video, track them and classify them. Artificial intelligence turns cameras into versatile and widely used IoT devices. This combination is the foundation of smart and safe cities.
The technology used in cameras has come a long way in the last decade. Image sensors now have increased resolution, lens speed, sensitivity, dynamic range, and a wide range of other parameters needed to produce high-quality images in all lighting conditions. Further development of digital technologies gradually opens up new opportunities for their usage.
CCTV cameras have become the eyes of the monitoring centers and sensors of silent, purpose-built machines. We are currently entering a new era where devices can recognize images. If it comprehends the meaning of images, it can drive a car, recognize faces, and measure, analyze, and control the world around it. With the advent of deep learning, the ability of machines to interpret visual data has been taken to a whole new level. With the help of massively parallel computing systems that model adaptable biological neural networks, it is suddenly possible to solve previously difficult problems for machines to understand, such as detecting vehicles or understanding the essence of visual scenes. Let’s take a look at some current use cases.
In many countries, the dramatic growth in car traffic seen in recent years is exceeding the capacity of the transport infrastructure. However, traffic congestion is not a good argument for leaving cars and switching to public transport. Increasing the capacity of road networks through civil works is an extremely costly and time-consuming solution. Another way to improve traffic flow on existing roads is to provide better traffic management.
A key element of the solution is adapting traffic lights at busy intersections, i.e., changing traffic light plans depending on the current traffic situation. Smart intersections use a network of sensors to continuously measure and evaluate green light demand, record long-term statistics, and communicate with the control system. Commonly used sensor types, such as induction loops or radar units, can also be replaced with a smart camera system.
Traffic smart cameras can detect vehicles, as well as pedestrians and cyclists, which have a significant impact on traffic. Smart CCTV systems can label objects and then transfer the labels to other cameras. This allows them to track objects and record their complete trajectory through an intersection (in the form of an O/D matrix). The control system analyzes the recorded trajectories and automatically identifies busy sections, traffic conflicts, or incidents (for example, accidents or stopped vehicles). In addition, smart cameras can also broadcast live video for visual monitoring in traffic monitoring centers or provide video recordings that can be used to determine the causes of traffic accidents. These features, along with their low cost, are why cameras are becoming increasingly popular in this field.
AI-based streetlights are one of the leading platforms where networks of urban IoT devices can prove their worth. IoT sensors provide ideal coverage of a city, a constant power source, and are typically managed by a single organization. Street lights provide the lighting needed by smart cameras. This means that a system of elements is directly optimized for collecting visual data using smart lighting.
How can it be used? There is a variety of ways: analyzing the use of public spaces, traffic control, and even increasing security with algorithms that detect abnormal situations. Does this sound like science fiction? AI-based modern cameras are tiny devices that are easily integrated into smart lamps. For one smart camera, the power required to run a processor in real-time neural networks is currently about 10 watts. This makes cameras a promising tool that can be used to develop smart streets.
Smart CCTV cameras have also become primary sensors of drones. The latter allows cameras to fly and create the perfect mobile monitoring system to offer a new perspective on things around us. Viewing a scene from a bird’s eye has many benefits. Obstacles to view are kept to a minimum, and the field of view ensures perfect ground target location accuracy.
The data from such a system can be so accurate that it can be used for in-depth analysis of traffic conflicts. For example, to analyze intersection safety by studying the interaction between individual actors in the traffic flow in a particular area. This technology makes it practically possible to detect accidents before they happen.
A 4K camera can cover up to 700 meters, which is long enough to detect aggressive behavior automatically, violations of the minimum distance between vehicles, etc. Like traditional CCTV camera systems, drones can be used in quantities to cover a larger area, like in a city. This approach is now being used in research projects.
Artificial intelligence developments in recognizing objects and understanding scenes have completely redefined the camera as a standard sensor that can now be used even outside the context of a smart city. As a result, the camera becomes truly smart with a wide range of use cases. Its accuracy is determined by hardware and mainly by algorithms based on extracting data from images.