Bank Security Cameras - What You Need to Know
Video surveillance with IP and CCTV cameras deters crime and captures evidence. But with AI-based video analytics, your surveillance system can become more proactive by analyzing video content in real time and notifying you or your security team of suspicious behaviors with real time alerts.
Video analytics, also known as camera analytics, are a feature of advanced video surveillance systems in which AI (Artificial Intelligence) analyzes moment-by-moment footage to provide actionable video content analysis and alerts.
Video analytics can be used to augment security with tools as varied as intrusion detection, people counting, facial recognition, license plate recognition, and even smart learning algorithms to support your video surveillance system with intelligence and perceptive abilities that have previously been limited to living beings.
There are three main types of video analytics: fixed algorithm analytics, facial recognition, and deep learning.
Fixed algorithm analytics (or fixed behavior analytics) use a pre-programmed step-by-step process to complete a predefined security-related function, such as video motion detection and object detection. A fixed behavior algorithm can create a real-time alert for security personnel if a restricted area is entered or if luggage is abandoned at a major international airport.
In recent years, face recognition technology has advanced so dramatically that camera analytics are able to make accurate 3D maps of faces and match identities against those in massive databases--as well as record new faces and store them for future reference.
This feature can also be used to track individuals in large and crowded areas and follow them from one camera's field of view to another's. And face recognition database memory can save time and a lot of expense, especially in the retail sector. For example, the face of a known shoplifter in the system's database will result in an alert if the shoplifter enters your business.
Deep learning is an AI-based video analytics solution which enables machines to recognize risks and predict future behaviors, differentiate between relevant data and data that can be ignored, and develop independent insights that human beings might miss.
Deep learning relies on artificial intelligence learning algorithms, a sophisticated method of machine learning in which computers develop the ability to comprehend complex and abstract ideas in much the same way that human beings do--by starting with the most basic information and building on that over time through exposure to increasingly complex concepts.
Deep learning usually begins with a yes-no process. For example, a computer may be taught to recognize outdoor and indoor environments by being shown many images and being asked if they are indoors. The computer would be given the correct yes or no answer every time it assesses an environment, and it would begin to recognize increasingly subtle factors such as lighting and background shapes and how likely they are to signify whether or not an image is indoors. A computer would learn that images with grass and plants are probably outdoors after being told that "yes" is the correct answer for many images with trees and grass.
But deep learning doesn't stop there. The computer would eventually be shown images of indoor environments with plants. When told that the image is indoors, the computer will understand that a plant is possible indoors and will assess the plant and other details of images so as not to make the same kind of mistake in the future. Eventually, through deep learning experiences, the computer will become as capable as a human being in its ability to recognize the sometimes subtle differences between inside and outside settings.
This "yes/no" process of deep machine learning enables AI-based surveillance programs to recognize objects and differentiate between them. This object detection can be useful when searching for missing items or objects such as weapons.
Object tracking is a feature in which AI-based video analytics recognize identifying characteristics of an object and predict its movement in each frame of a surveillance video--including movement from camera view to camera view and blind spots between cameras.
Video analytics work well both indoors and outdoors and can support such public safety needs as park security and crowd control.
Features such as crowd detection and people counting enable analytic video surveillance to assist in crowd management by drawing attention to spaces that are overcapacity and by offering suggestions about where to dispatch more security.
Similarly, loitering detection video analytics can draw attention to suspicious behaviors or areas that need to be cleared. This can be useful in many situations, including parking lot monitoring and even the protection of critical infrastructures. Once, on a long day visiting the mall in Washington, DC, I felt tired and decided to take a nap on the steps of the Supreme Court. The moment after I lay back and closed my eyes, I heard a voice telling me that people are not allowed to sleep on the Supreme Court's steps. It was a security guard. The rapid response suggests that AI-based video analytics were taught to recognize behaviors that signify future nap-taking. The system probably alerted security the moment I sat sidewise on the step and placed my bag where I intended to lay my head.
Video analytics can improve safety and reduce security staffing costs by helping personnel to monitor large areas more efficiently. Such features as facial recognition and object tracking can even monitor individuals as they move from one camera's field of view to another's.
In large CCTV video surveillance systems, there are usually many more security cameras than there are security professionals monitoring their channels. Instead of requiring human eyes to rapidly glance between channels on the off-chance of spotting an irregularity, network cameras with video analytics can focus with superhuman attention on multiple video surveillance channels at once and differentiate between situations that require the attention of security personnel and situations that are OK. For example, upon noticing entry to a restricted area, video analytic software may use face recognition to determine whether the entry is authorized or requires an alert. Security staff are then free to focus on what humans do best: review flagged surveillance videos and make final judgment calls on appropriate next steps.
With tools such as license plate recognition and object tracking, there is a video analytics solution for most public safety needs, including traffic control.
Video analytics can support traffic control by monitoring vehicle movements, assessing vehicle registration plates and other markings, and monitoring traffic jams. Camera analysis can even manage traffic light control systems for a more flexible process with less backed up traffic.
License plate recognition is often used to ensure that drivers receive tickets for speeding through red lights. And wrong-way detection video analytics can provide warnings on highway ramps for traffic going in the wrong direction, as well as alert oncoming cars. Some video surveillance systems with AI-based video analytics even have the capacity to recognize reckless driving and capture the evidence.
Video analytics solutions are also central to operational processes of business, government, and residential institutions. For example, automatic license plate recognition is often at the core of gate entry security systems.
Video analytic technology such as face recognition can be extremely valuable to business and government entities where foolproof access control is essential. For example, instead of depending solely on smart cards (which can be stolen) to verify authorizations, a facial recognition protocol can augment security and prevent the misuse of stolen smart cards.
Artificial intelligence has developed rapidly in recent years to reduce the toll of vandalism, theft, and other crimes. With video analytics, any security domain can be monitored more effectively with fewer security professionals. Machine learning offers new solutions that can dramatically augment security in your home or business.