Day: March 22, 2021

Rahat Jain Managing Director at IDIS India In recent years, the terms ‘intelligent’ and ‘artificial intelligence (AI)’ have been applied to many different types of security systems, but with little apparent agreement when it comes to a precise definition of what AI is. This is despite the fact that 71% of security professionals report that AI video analytics already provides value to their operations or that they expect it to in the future, according to the IFSEC Global Video Surveillance Report 2020, which analyzed feedback from over 700 security respondents globally. So, it’s important to understand that not all solutions labelled as ‘intelligent’ or ‘powered by AI’ are designed to the same standard or deliver equal value. Many of the early iterations of video analytics rely on Binary Large Object (BLOB) technology. This is found on most modern IP cameras, which is why they are commonly referred to as ‘blob type’ analytics. These are formulated to detect an event such as a virtual line cross; they detect and track objects as ‘motion blobs’ and distinguish them from smaller binary objects. For many applications these are still useful. But video analytics capability has moved considerably beyond this. Today security departments can take advantage of deep learning that leverages neural networks made up of multiple layers of algorithms and advanced processing. This is now driving what is widely accepted to mean true intelligent video analytics. Deep learning engines are ‘trained’ using vast datasets of images and video footage of people, objects and vehicles. They can ‘look for’ size, shape, speed and directional information, and they continue to learn while in use. To an extent, deep learning replicates the way neurons work in the brain – it can analyze and prioritize input from video data to decide which inputs are of value, and it will notify security operatives accordingly. Deep learning’s real value comes from being able to detect suspicious activity or unusual events and eliminate those smaller binary objects that are just ‘noise’ and apply rules that meet with specific applications and operational requirements. In addition, deep learning should enable users to use metadata to search multiple camera streams to find the most accurate matches for persons or vehicles of interest within minutes. But again, some caution is needed. Deep learning video offerings can still disappoint, generally as a result of having been launched too early, before engines were fully trained and able to recognize objects reliably and accurately. Systems integrators need to exercise caution regarding claims and jargon. They need to understand which offerings and which functions will genuinely add value for their customers, and help them to increase productivity, provide useful business intelligence, and ensure they deliver RoI, long term. Overcoming the Common Challenges Caused by False Alarms The 2020 IFSEC Global Video Surveillance report cited reducing false alarms as the #1 reason for adopting AI – and for good reason. Traditional blob type analytics cameras are prone to being triggered by environmental factors such as heavy rain, snow or moving foliage, and struggle to distinguish a human presence, which may present a threat from harmless animal activity. For users, this can result in time being wasted investigating the cause of alarms, and the larger the site, or more overstretched the system operator, the worse that problem can be. ALARM OVERLOAD Alarm overload is a common problem. Operators can quickly become desensitized by false alarms and can start missing genuine threats, or even be tempted to shut off the system. Alarm receiving centers and virtual guarding firms typically increase charges for more frequent call outs and they may even withdraw monitoring services from problematic sites until cameras are re-configured or replaced. This can result in organizations needing to draft in additional security officers, to maintain protection, or risk leaving gaps in security. Over time, many organizations find it unfeasible to maintain systems that are prone to false alarms. The solution? By moving to deep learning-based analytics, customers can attain improved situational awareness, with highly accurate AI-assisted notifications for intrusion, object, loitering, and unusual event detection. Security operators will be better able to manage everyday events, and respond to more serious threats and emergencies. In short, safety and security are enhanced by better detection and verification. ELIMINATING OPERATOR FATIGUE AND INCREASING EFFICIENCY Unlike human brains deep learning engines don’t get tired. They can constantly monitor multiple camera streams in search of suspicious behavior, maintaining performance levels even in the busiest scenes such as retail malls, logistics centers, higher education settings and outdoor spaces. Relying on human operators to monitor multiple cameras means hiring enough staff to cope and allowing for regular breaks to ensure they stay alert. Using AI-assisted notifications free-up operators from having to constantly monitor multiple camera streams and video walls. Instead, they can respond quickly and flexibly, and not just from the control room. They can configure alarms to be received to client software, and on mobile devices such as smartphones and tablets, giving the ability to verify and respond to events on the move. Improving the ability of security managers to oversee security operations away from the control room – by giving them more accurate information along with powerful VMS functionality and tools – lets them better manage incidents on the ground and direct their teams. The latest generation of AI-assisted tools can transform the work of security teams. Today’s truly smart video technology can allow security provision to be better focused, with officers being re-deployed to more important tasks that add greater value to their roles, for example giving them time to engage with the people they are helping to safeguard rather than remaining unseen in the control room. And strategically, heads of security can interpret and use accurate real-time and historical data to drive more informed decision-making, to better mitigate risk across their enterprise. Speeding Up Investigations with The Power of Metadata Deep learning and intelligent video analytics capture metadata even when analytics rules are not applied, meaning that users can benefit from advanced…