Revolutionising Security:
Embracing AI as the Foundation

In the traditional approach to security nstallations, decisions were made based on standard criteria: selecting cameras according to technical specifications, choosing processing hardware capable of handling data streams, and implementing a Video Management System (VMS) for centralised control.

If budget permits, video analytics might be integrated to enhance surveillance capabilities.

However, this method often lacked agility and adaptability, as it relied heavily on static systems and predefined rules.

Security measures were reactive, responding to incidents after they occurred, rather than proactively anticipating and preventing them.

This approach could leave vulnerabilities unaddressed until they were exploited, highlighting the need for a more dynamic and responsive solution.

The emergence of artificial intelligence (AI) has ushered in a new era of security solutions, revolutionising the way we approach safety and surveillance.

Recent advancements in AI technology have transformed industries and everyday life, from predictive analytics in finance to natural language processing in customer service. In the realm of security, AI-powered analytics have become a game-changer, offering unprecedented capabilities in threat detection and response.

By harnessing the power of machine learning and deep learning algorithms, security systems can now adapt and evolve in real-time, continuously learning from data to identify patterns and anomalies.

This shift towards AI-driven solutions marks a fundamental change in the security paradigm, where proactive prevention takes precedence over-reactive measures. Instead of simply reacting to incidents as they occur, security systems equipped with AI can anticipate and mitigate risks before they escalate, significantly enhancing overall effectiveness and resilience.

iSentry is an Artificial Intelligence-powered video analysis platform.

It can be installed on new systems as well as the vast majority of existing CCTV systems.

After a norm is established for a particular scene, the system will then create alerts based on exceptions or ‘events of interest.’ iSentry then classifies each event upon detection using Deep Learning tools and a logic engine.

This provides instant situational context to control room operators so that they can better understand what they are being shown, allowing them to respond appropriately.

iSentry can, for example, know that five or six men huddled around an ATM for a prolonged period may be an event of interest and it can differentiate and understand that a mother standing at the same ATM with her children is not a reason for alarm.

iSentry detects loitering, directional violation, unusual objects entering a scene, running, violence, tailgating, smoke and fire, major leakages, removed or introduced static objects, people climbing walls, entering a perimeter or area, or graffiti painting. iSentry can also carry out pose analysis (whether someone is standing, sitting, lying on the ground or has fallen), and many other abnormal situations.

iSentry´s powerful Logic Engine can largely and autonomously fulfill the function of a video surveillance operator.

More than 80% of the time it will be capable of reaching a correct decision regarding an event of interest, based on the number and combination of object types that trigger an alert, the time of day and object size, or even the likelihood of accurate classification.

Any incident that iSentry cannot confidently classify automatically or determine through the rules engine whether to dismiss or alarm is then transferred to a human operator for further investigation and decision-making. iSentry empowers control room operators to solely focus on those decisions at which humans excel.

iSentry also enables control rooms to function effectively with far fewer operators, as massive quantities of video can be meaningfully and accurately monitored, and processed by the platform.

Equipped with the latest AI algorithms, iSentry quickly learns what is normal from an individual CCTV camera feed, so that it can then detect the abnormal. It can be deployed in systems from just a few cameras up to thousands of cameras.

The integration of unusual detection into security systems offers several key benefits. Firstly, it enhances threat detection capabilities by identifying deviations from normal behaviour patterns, even in the absence of predefined rules or signatures.

This proactive approach allows security personnel to stay ahead of potential threats, mitigating risks before they escalate.

Secondly, anomaly detection reduces false positives by focusing on genuine deviations that warrant investigation, minimising unnecessary alerts and conserving resources. This streamlines operational workflows and ensures that security personnel can prioritise responses effectively

Moreover, anomaly detection enables continuous learning and adaptation, as AI algorithms evolve based on feedback and new data. This ensures that security systems remain agile and responsive in the face of evolving threats, without the need for manual intervention or rule updates.

In conclusion, the integration of AI-driven anomaly detection into security systems represents a significant leap forward in safeguarding assets and ensuring public safety.

All in all, we are committed to championing this change paradigm, where AI serves as the cornerstone of innovation and effectiveness in security operations.

Beyond enhancing security effectiveness, the adoption of AI in CCTV systems brings notable sustainability benefits.

One key advantage is the reduction in physical infrastructure requirements. AI-powered analytics enable fewer cameras to cover larger areas effectively. This translates to fewer resources needed for camera installation, including reduced wiring and lower demand for active network devices, thereby decreasing energy consumption and material usage.

Moreover, the intelligent capabilities of AI can lead to a decrease in the total number of cameras required. By efficiently monitoring and analysing video feeds, AI algorithms can identify areas of interest and focus surveillance efforts where they are most needed, eliminating the need for excessive camera deployments.

This not only reduces the environmental footprint associated with manufacturing and deploying additional cameras but also minimises the visual impact on the surrounding environment.

Additionally, the implementation of AI in CCTV systems can lower labour costs associated with surveillance operations.

With AI-driven analytics automating tasks such as monitoring video feeds, detecting anomalies, and issuing alerts, the need for human intervention is significantly reduced.

This results in fewer personnel required for surveillance activities, decreased reliance on transportation for on-site patrols, and ultimately, lower operational expenses.

Furthermore, the reduced need for physical infrastructure and personnel translates to decreased energy consumption, leading to lower carbon dioxide emissions associated with the operation of CCTV systems.

By optimising resource usage and minimising unnecessary activities, AI-driven CCTV systems contribute to a more environmentally sustainable approach to security surveillance, aligning with global efforts to mitigate climate change and reduce greenhouse gas emissions.