Artificial Intelligence is significantly transforming border security, providing sophisticated tools to bolster safety and efficiency at international boundaries. Innovations in AI, such as machine learning algorithms and automated surveillance systems, enable more accurate monitoring and quicker identification of threats, ranging from illegal crossings to potential security breaches. Predictive analytics further enhances border control by forecasting risk patterns and streamlining resource allocation. However, the deployment of these technologies also raises critical ethical and privacy concerns, necessitating the development of robust regulatory frameworks that protect civil liberties while ensuring security. The integration of AI into border security not only tightens national defenses but also fosters international cooperation, paving the way for a globally unified and advanced border management system.

IntelexVision’s iSentry,
an AI-powered platform  across global border control operations could significantly enhance security measures by detecting unusual behaviour efficiently. iSentry’s capabilities can be adapted to monitor and secure borders, protecting people, property, and national assets. iSentry analyses  video in real-time to identify any anomalies, such as illegal crossings, smuggling. This proactive approach also aids in the efficient allocation of resources, making border control operations more effective and responsive to dynamic security challenges.

Border control security faces numerous challenges that complicate the protection and management of international boundaries. One major issue is the vastness and diversity of the terrain along borders, which can range from dense forests to vast deserts, making consistent surveillance difficult. Additionally, the sheer volume of people and goods crossing borders daily demands robust yet rapid screening processes to prevent illegal activities without causing delays. Human error and the limitation of manual monitoring can also lead to oversights, allowing security breaches to occur. Political and social pressures further complicate the deployment of effective border control measures, requiring solutions that are both efficient and sensitive to humanitarian concerns.

Integrating AI video analytics, such as the iSentry system, into border control operations presents a proactive approach to mitigating these risks. By leveraging AI to continuously and accurately analyse video feeds, iSentry can detect unusual behaviours and potential threats in real-time across challenging and diverse environments. This capability not only enhances the detection rates of illegal activities but also reduces the reliance on human monitoring, thereby decreasing the chances of error. Moreover, AI-driven systems like iSentry can adapt and learn from new patterns of behaviour, ensuring that border control agencies are always ahead of the curve in threat detection. This sophisticated technology supports a more secure border environment, while also enabling more efficient use of resources and improving response times to potential incidents, thus balancing security needs with operational efficiency.

iSentry from IntelexVision can address several critical use cases in theborder control, enhancing security and operational efficiency. Here are some specific applications:

Automated Entry and Exit Checks: AI can be utilised to automate the entry/exit processes at border checkpoints. By integrating facial recognition, AI systems can swiftly verify identities and travel documents, significantly speeding up the process whilst reducing human error.

Anomaly Detection in Crowd Behaviours: iSentry system can monitor large groups of people at border crossings or within border areas for unusual behaviours, like loitering, moving against the flow of traffic, or engaging in suspicious interactions, allowing security personnel to respond proactively before a potential incident escalates.

Predictive Threat Analysis: Leveraging AI to analyse historical data and current trends, border control agencies can predict and prepare for periods of increased risk, such as potential surges in illegal border crossings or smuggling activities. Predictive analytics can help allocate resources more effectively.

Surveillance of Remote and Rugged Terrains: CCTV cameras equipped with long distance iSentry detection can be used to patrol remote and difficult-to-access border areas. These footages can be analysed in real-time to detect unauthorised entries or suspicious activities in areas where traditional surveillance is challenging, thus extending the reach of border security without the need for extensive human patrols.

Cargo and Vehicle Inspection: AI can streamline and enhance the security of cargo and vehicle inspections at borders. By using image recognition and scanning technologies, AI systems can quickly identify hidden compartments, suspicious cargo, and other anomalies that may indicate smuggling or trafficking activities. This not only speeds up the inspection process but also increases its accuracy and effectiveness.

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.

Equipped with the latest Artificial Intelligence (AI) and Neural Networks based Machine Learning 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.

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, recognise that five or six men huddled around a valve for a prolonged period may be an event of interest, and it can differentiate and understand that staff are performing necessary maintenance.

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/signs 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.

Its Deep Learning engine recognises multiple classes of objects even at difficult angles. Additional capabilities have recently been added such as the ability to monitor for Health & Safety compliance, for example, the wearing of hard hats, high visibility jackets, eye protection glasses, face shields or facemasks for COVID compliance.

iSentry´s powerful Logic Engine can largely and autonomously fulfil 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.

To reduce the number of false alarms many Video or Alarm Monitoring Centres use Video Verification Platforms to determine if there are people or vehicles in specific snapshots of alerts, typically generated by on-camera motion detection analytics. This can lead to a reduction in overall alarms by as much as 50-70%.

While this sounds impressive, control room operators are still left with an insurmountable quantity of false alarms to deal with on every shift.

The iSentry platform uses AI to first detect and then intelligently classify alerts. As a result, the number of alerts control room operators receive is 90-95% lower than standard video verification systems. With iSentry, fewer operators can perform more, and to a much higher standard, freeing personnel to work on other higher value-added tasks.

We tested iSentry’s Unusual Behaviour on a 25-camera system against traditional video verification software platforms. iSentry escalated 2,086 alerts to operators, against 32,616 alerts from standard video verification software. iSentry also reduced escalated operator alerts by 94% greatly increasing efficiency and lowering alert-related operational costs.

In a major city, iSentry monitors many thousands of cameras in real-time. Our system’s AI and Deep Learning powered alert detection has become so precise that our system now only presents 1% of total recorded video to a control room operator for review.

The accuracy of their recall of events of interest flagged by iSentry is 90%+. Prior to our system being installed, this was between just 5% and 10%.

Prior to iSentry’s installation, one control room operator was responsible for managing up to 80 cameras with very poor incident detection performance. With iSentry, the same operator can monitor a daily average of 350 cameras and up to 800 cameras at off-peak times.

iSentry was installed by a major bank across hundreds of branches with an average of 30 CCTV cameras per branch. As a result the total amount of video to be reviewed was reduced to less than 1% of the previous quantity, without missing any event of interest.

This significant reduction in video footage and the efficient hardware and software architecture of iSentry has led to very low bandwidth demands on the central Control Room together with a reduced need for operator consoles. This has led to major CAPEX savings.