In the past few years, artificial intelligence has taken the tech world by storm. In almost every industry you can think of, AI is being used to automate processes and make things faster and more efficient. The physical security industry is no different. AI has the potential to transform this niche by automating many processes that are done manually today and making greater use of cameras at the same time. Security cameras are one area where AI is making a big impact. There are several ways that artificial intelligence is transforming this niche. Here are the top 3 ways AI is changing the security camera industry.
AI for automatic license plate recognition
Automatic license plate recognition (ALPR) is the use of computer vision to extract data from images. In the security camera world, ALPR is used for things like licence plate tracking, stolen car detection, and speeding infringements. A security camera with an ALPR system can scan plates in real-time and record the data. This is especially useful when a crime or traffic violation is taking place or when you want to drive automation. You can use the cameras to get the licence plate number and other identifying information of the vehicle to build your use case.
There are two ways that AI is changing the way security cameras use ALPR. The first is with improved accuracy. The second is with lowering the cost and running directly on the camera. ALPR has been around for a long time, with the optical character recognition (OCR) library Tesseract first being developed in 1985. AI has greatly improved the accuracy of ALPR by being able to more accurately detect a vehicle and then the specific area where the plate is located to feed into the OCR engine. This is very important to reduce the number of false reads, as lighting, dirty plates and different plate designs are challenges that need to be overcome. Even the OCR engine is now driven by AI, with Tesseract using an LSTM neural network.
These advances in AI have resulted in a reduction in the hardware required for accurate plate readings. Now AI models can use data from cheap cameras, low lighting and larger angles of incident, meaning for general surveillance you can use existing cameras. A key point to note is that to detect plates at night, the camera must have adequate infrared(IR) to illuminate the plate at night and not be distorted by the headlights. We use these advances in AI to solve the real-world problems of Chain of Responsibility and Dock Management.
AI for motion detection and anomaly detection
Motion detection is when a camera is programmed to send an alert when motion is detected. Traditionally, this process was done by looking at a change in pixels and then verified by humans. Someone would have to sit and stare at the cameras and wait for something to happen. Autonomous cameras use AI for motion detection on things you care about, people and vehicles. This means that there is no need for a human to watch and receive all motion events from a camera.
Instead, the camera uses AI to detect the motion of an object of interest and send an alert when it happens. These cameras use machine learning to train and improve over time to learn your exact scene. This means that the more data it processes, the smarter it gets. A security camera using AI for motion detection can also detect anomalies. Anomaly detection is when the computer program looks for patterns that don’t belong. The best anomaly detection is still done by people, a way that AI and people can work together for a better outcome.
AI for cloud storage of videos and images
Traditionally, security cameras store their data on an internal hard drive. When a crime is committed, the data is collected from on-site servers and sent via hardware, email or online storage services. Accessing footage securely and remotely from the site can be a challenge. The main reason video has always been stored on-site is the large amount of video data being stored with basic motion-based storage. Advanced cameras use AI to automatically send videos and images to the cloud when they are triggered by objects of interest, not just motion. This way, storing large quantities of video is not required. This is a huge advantage since it reduces the cost of bandwidth and cloud storage.
Finally having cloud labelled data of objects of interest from multiple sites has huge advantages for training more accurate AI models. The event recognition and cloud storage will improve over time until a tipping point of accuracy allows peace of mind that no critical events will be missed. This inflexion point is not far away and will have a profound effect on the whole security industry.
Conclusion
The security camera industry is undergoing a massive transformation thanks to AI. AI is making it possible for cameras to do things that were once impossible. It can analyze large amounts of data, identify patterns, and make decisions based on those patterns. AI is being used for things like automatic license plate recognition, motion detection, and anomaly detection. To see how you can use this AI, book a demo today.
