Most companies are gathering trillions of bytes of data, day after day, at no small cost, and then doing very little with it. Worse still, the data often is not serving its primary function very cost effectively. The “culprit,” so to speak, is video surveillance data, the information captured by the video cameras that are used throughout most modern facilities. But the situation is changing rapidly, thanks to the new landscape of Video AI Analytics Applications. What is Video Analytics? Video Analytics can be described as “the emerging technology where computer vision is used to filter and manage real time CCTV video for security and intelligent traffic monitoring.” Simply put, Video Analytics is an automated approach to managing and analyzing video, without the cost or man-hours previously required. There are many different brands and technology platforms for Video Analytics, but they all work on the same basic principles, using pattern recognition and other Algorithms technology to provide two critical capabilities: Recognize unusual activities as they happen and notify the security system in real-time. Functionality Today’s Video Analytics software offers growing functionality. For example, it can be programmed to look for specifically defined anomalies. It can even be programmed to give special attention to specific elements in a video frame—such as a computer, door, or filing cabinet. Video Analytics software tracks people and objects, and can send alarms when suspicious activities occur. Furthermore, Video Analytics can be integrated with other security and information systems to create new possibilities for using and managing video data. The first step for most companies is to use Video Analytics to support and enhance guard performance. In this application, Video Analytics software continuously monitors everything that happens in the field of vision of every surveillance camera, every second of every day. When it sees suspicious or unusual patterns and activities, it sends an alert to the security system so guards can look at the monitors, see first-hand what is happening, and take any needed immediate action. It can also trigger other security events, such as coordinating the motion of several cameras to track the movement of a suspect through a facility. In some programs this is called “guard service.” Guard service should make every guard more effective, so companies find they can improve security and reduce staff at the same time. Recent studies show that it’s common for guard service applications to generate savings of 75% or more in total guard costs. Adding on to that value, Video Analytics can then be linked to card access systems to improve security. A card being used in an unauthorized or suspicious way can trigger cameras to zero in on the event and record the time and other information in a searchable video log. The real power of Video Analytics may be the fact that it turns analog video into useable data that can be analyzed, searched and managed. This opens endless possibilities for guiding decisions on facility use, energy consumption, personnel safety, and many other issues. Vitally is the improved response time. You can simply find information and act on it more quickly with Video Analytics, whether the problem is a break-in happening right now, or a building use problem that pops up every day. Key Features of Video Analytics Value with Video Analytics: Augment staff and improve camera investment ROI by extracting key information from captured video to uncover insights and patterns. Create a Security Model: Customize the “monitor and alert” parameters from live-streaming fixed cameras to help identify perimeter breaches, abandoned objects and more. Find the Buried Pictures: Save time when searching for relevant images. Advanced content-based algorithms for detection improve time and accuracy of cross-correlation and trend analysis. After collecting and storing petabytes of video data, organizations now want more value out of their investment. Video Analytics provides an answer, helping most companies improve security and lower costs. By starting with applications that deliver rapid ROI, like guard enhancement, companies can implement the technology in a way that pays for itself. The value can then be extended to other security and information systems, leveraging many technology investments to improve security and building management.
Video Analytics and Theft Imagine a manufacturing company with an 80,000 square foot plant, multiple exits and shipping docks, 200 employees and several products which are as small as the palm of a hand or as big as the flatbed of a pickup truck. Obviously a lot of elements to this scenario. Now imagine there has been theft which has been taking place during and after hours of operation. This threat has been going on for at least six months and product loss has started to affect cost of goods. This is where video analytics comes into play. Now, there might (actually should) be video cameras set up throughout the facility, but they are missing the video analytics component. With video analytics, the possibilities of detection are endless and evolving every day. Anomaly Detection The specific package for this scenario could be “Anomaly Detection.” Anomaly detection has the ability to pick up on something that is not deemed normal in any environment. For example, if all employees are wearing uniforms, but there’s one individual who is not wearing a uniform and is hanging around an exit door or shipping document, this would send an alert for security to investigate. Or another example would be if running wasn’t allowed in the facility, and cameras show two or three individuals (wearing uniforms) running towards an exit door. Is there a fire, emergency or are they actually trying to steal something? Another possibility is there is supposed to be 7 employees in a designated area and there are only 6 or more than 7, anomaly detection has the ability to send out an alert to specific personnel to investigate the abnormal activity. Facial Recognition Another package for this scenario could be “Facial Recognition.” Maybe the facility doesn’t have uniforms for its employees, but through the use of Facial recognition, the organization knows exactly who’s working for the company and where their work-station is. Each morning or evening when employees walk, facial recognition could identify all the certified employees. However, what if one person wasn’t identified. Who is he/she? Where is he/she? What is he/she doing? Again, another alert could be sent to Security to investigate. Maybe there’s a specific section of the plant where the majority of theft is taking place. Facial Recognition could identify the employees who have that work station and Anomaly detection might be able to identify abnormal activity from a specific employee or employees. The options and scenarios are endless and over time, video analytics has the ability to self-learn. By Machine Learning, the ability to identify abnormal activities will continue to multiply and also identify exactly who is supposed to be (and not supposed to be) where and when. This is just one example where video analytics could be useful in so many ways; safety, cost-efficiency, theft prevention, security, etc. More Scenarios A casino would be able to identify when a ‘high-roller’ walks into their establishment. The concierge would greet that person with the ‘VIP’ treatment (i.e. favorite beverage, favorite table, etc.). It would also be able to identify abnormalities which could spot card-counters or cheating in anyway. Underage gambling may also be reduced due to facial recognition. Addicted gamblers could be removed from the building if they put themselves on a self-ban list. An issue at the forefront right now and probably the most important issue at hand is children’s safety in schools. Anomaly detection would be able to detect if a person should or should not be in a specific location of the school. Facial recognition could identify whether the person is a student, employee or security guard. If not, alerts can be executed and initiate all sorts of preventative measures (classroom doors auto-lock, 911 notified, security guards sent to the specific area, etc.). What about school buses? Facial recognition would be able to identify and count the number of children on a specific bus. If John or Jane Smith is not on the bus, an alert would be notified. Anomaly detection could identify bullying or fighting or any abnormal behavior taking place on the bus. What if a random person tried to board the bus? Again, the options are endless. There will be a lot of learning from a human standpoint and a machine learning standpoint. As listed here, I think we all would agree there are certain areas which need to be implemented as soon as possible.