Advantages of Using Artificial Intelligence in Video Surveillance

AI video analytics has gained immense popularity due to a large number of real-world use cases ranging from identifying different objects and detecting anomalous behavior patterns to face recognition and traffic flow management. Over the past couple of years, this technology has advanced significantly, now offering customers greater functionality, impressive accuracy and lower prices. This explains its wide acceptance and apparent massive growth. According to a recent projection, the AI video analytics market is set to grow into a $22 billion industry by 2027.

By and large, we can see a tendency to modernize security infrastructure through the integration of AI video analytics into existing video surveillance systems. What are the reasons? In this blog article, we’ll discuss the actual advantages of using AI technologies from a security perspective.

Increased efficiency of security operators

The main function of video surveillance is to observe public places or the perimeter of secure facilities in order to protect them from theft, intrusion, fire, and any other possible danger. Security cameras with integrated video surveillance systems are installed in all important locations of the site so that security operators could constantly monitor all the areas looking for specific behaviors that may indicate an emergency or a potentially dangerous situation that needs intervention. No matter how good the cameras and VMS are, the weakest link in the security system is the human operators. The problem is that people can’t keep an eye on multiple screens all the time without losing concentration, which eventually can lead to missing critical events. On top of that, people are prone to fatigue, which inevitably results in a decline in productivity and response time and increases the possibility of human error.

Integrating advanced AI-powered solutions into video surveillance systems allows security staff to keep watch over the entire premises 24/7 and have better situational awareness without having to be glued to monitors all the time. AI automatically detects threats, filters out false alarms and notifies operators of real dangers so that they can take proper action. AI-powered threat detection solutions work equally well day and night, detect objects at a large distance, process a large amount of video feeds in a fraction of a second and implement advanced analytics in real time. Thus, due to advanced analytics integrated into video surveillance systems, security officers get better, faster and more accurate real-time data and can make a more informed decision on further actions.

Video surveillance has long been criticized as a reactive technology that is only useful for forensic review after an incident has already occurred and is deemed unsuitable for real-time threat detection. Back in the days, security teams had to rely on external security cues and plan further measures accordingly. This involved manually monitoring cameras after being informed of a potential attack, which in most cases was futile.

Contemporary video surveillance makes use of AI video analytics and is capable of detecting and reporting anomalies within a fraction of a second, thus making it a proactive security system. This is important for public places with heavy footfall and large campuses, such as airports, retail outlets and tourist hotspots. These places have hundreds of cameras, so non-AI video surveillance would be impractical.

Detection of a wide variety of threats

● Powerful Intrusion Detection

According to the FBI, over 57.5% of burglaries involve forceful entry, 6.3% are forced entry attempts, and 36.2% involve unlawful entrance into protected properties. Therefore, a powerful intrusion detection system is the first line of defense and an invaluable tool for any organization or individual. AI makes it more effective due to the automation of threat detection and negligible false positives, which enables security personnel to focus timely on what really matters. Another important factor is immediate real-time alerts with all the necessary information about the event, which helps to increase situational awareness and significantly reduce response time to the threat.

Motion detection cameras used without AI video analytics are unintelligent devices that alert against everything that moves, be it an animal or even a shadow change. These cameras can raise anywhere between 100 and 150 false alerts per camera per day, which is quite a burden for sites with hundreds of security cameras installed to monitor thousands of visitors. This is when false alarm filtering technology comes to the rescue and helps minimize the overwhelming number of false alarms to just a few, allowing operators to focus on handling real alarms.

If an intrusion is detected, the system sends essential visuals and metadata to the security unit, which enables them to take prompt action. It can also be configured with access management tools to lock certain parts of the premises in case intrusion is detected. In the case of banks, it can be configured to lock the bank vaults and other zones. On the other hand, schools and colleges can also use it to automatically seal classrooms if an intruder or gunman is detected. With object tracking technology, the gunman can be tracked on campus in real time even after the weapon has been hidden.

● Smart Object Detection

AI and computer vision are now being increasingly utilized to identify a wide variety of objects. When it comes to weapon detection, it gives security teams the advantage of advance warning before a shot is even fired and enables them to proactively respond to a threat. The system instantly alerts on-site security and law enforcement, providing video data about the type of weapon, time and location of the event, which enables them to proactively respond to a threat. Besides, the system can be configured to trigger automated pre-defined safety protocols and even lock the area down if it is verified by assigned personnel.

AI technology also enables accurate real-time detection of unattended objects like baggage. This has become a real help for security personnel responsible for monitoring public places, especially airports, retail stores or educational facilities. The same technology is applied to detect multiple foreign object debris (FODs) on an airfield or to automatically detect discarded rubbish in recreational areas or litter thrown from cars. As soon as a threat is identified, an alert is immediately sent to all the assigned endpoints to initiate an appropriate response.

Finally, AI is used for face mask usage detection as well as to check that workers have their protective equipment on. None of that is possible with cameras that are not powered by AI analytics unless a large team of security personnel is deployed to check real-time video streams, which is costly and impractical.

● Abnormal Behavior

The FBI reported that over 73% of property crimes in the US included some form of larceny that led to a total loss of over $5.9 billion. Of those larceny crimes, over 22% included shoplifting and thus summed up to about $1.3 billion in reported losses. According to yet another study reportedly issued by the National Retail Federation Survey, US retailers lose over $50 million annually due to theft and shoplifting accounts for about 36% of that. This menace can be curbed utilizing AI video analytics designed to detect suspicious consumer behavior that may result in shoplifting.

AI-powered video surveillance is also a useful tool for the detection of aggressive behavior such as fights, acts of vandalism or violence. When notified about these events, security can intervene just in time before the situation escalates so as to minimize negative consequences and probably prevent a tragedy.

Particular attention is now placed on computer vision-based solutions that can detect smoke and fire at a greater distance based on the images from thermal cameras, thus ensuring early detection of the danger.

AI analytics integrated with thermal cameras also helps manufacturers implement smart temperature screening of machinery, thermal scanning for lithium-ion battery overheating, heating of transformers and electrical substations, thus protecting valuable assets and minimizing accident hazards to employees.

Faster video search

Reactive security measures such as looking through video footage to get more information about events forensically might seem easy, but without AI video analytics, it can be extremely difficult and time-consuming. Security personnel working at large campuses and business premises like airports, universities, hypermarkets and other similar places would agree with this.

After all, such places have hundreds of cameras installed, and each camera runs 24/7, accumulating an enormous amount of video footage and making searching for this data time-consuming and costly. Nonetheless, it is essential for investigations and law enforcement officers, and that’s when AI video analytics can be of great use.

Contemporary AI-based video surveillance systems enable the user to search for a specific action or object in all the footage much faster than a human operator. Engaging AI in forensic analysis drastically reduces the amount of time that goes into extracting insightful data to investigate issues.

Another obvious advantage of AI-powered person search is the ability to look for people without relying on facial biometrics. This means that the system will detect the person of interest even if they are not facing the camera or if their face is hidden under a mask or a hoodie. Low-quality photos will not hinder the search process either. Obviously, when the search is based on a physical description of a person involved in an event, false positives are inevitable as there might be several people in the view of the camera wearing similar clothes. But even if a few people with similar appearances are detected, it will be of good help to investigators. Just a quick manual verification will help them minimize errors and find a person across the entire facility within a minute.

Business insights

Video analytics can also be used to detect trends and patterns and analyze historical data to drive business insights. This data enables businesses to make better decisions, optimize their operations, eliminate choke points and improve profitability. Take, for example, retailers who use AI for traffic flow analysis to collect data related to the movement of people across their facility and create heat maps for smarter consumer behavior analytics. It allows them to get actionable customer insights and predict product demand, estimate the effectiveness of store performance and take action to optimize operations and improve profitability. One strategy is to use in-store traffic analytics to identify areas where people spend the most time. This helps understand popular locations and products and use this data to optimize product placement, for example, by putting higher profit margin items where people focus their time. Besides, heatmaps for foot traffic can assist with layout optimization and staffing decisions, such as increasing the number of employees at peak times. This can minimize waiting times in queues and ultimately enhance the customer experience.

With smart software, it is easier to understand trends and assess the effectiveness of marketing efforts. AI analytics can generate insights based on demographic traits such as gender, age, customers’ interests and habits. With all these data, it is easier to meet customers’ needs and provide them with more relevant and personalized product recommendations.

Collected statistics can also help identify vulnerable spots in the store where most shoplifting cases occur and improve security efforts there for effective loss prevention.