{"id":5218,"date":"2026-10-27T10:55:43","date_gmt":"2026-10-27T02:55:43","guid":{"rendered":"https:\/\/csecrosscom.net\/au\/?post_type=insights&p=5218"},"modified":"2026-10-27T10:58:19","modified_gmt":"2026-10-27T02:58:19","slug":"how-custom-ai-can-support-safer-smarter-campus-operations","status":"publish","type":"insights","link":"https:\/\/csecrosscom.net\/au\/insights\/how-custom-ai-can-support-safer-smarter-campus-operations\/","title":{"rendered":"Beyond Standard Video Analytics: How Custom AI Can Support Safer, Smarter Campus Operations"},"content":{"rendered":"\n

Most CCTV analytics platforms are designed to recognise common object types such as people, vehicles and bicycles. While this supports many security applications, universities often need greater visibility of assets, behaviours and activities that fall outside standard object classifications.<\/p>\n\n\n\n

Across lecture theatres, libraries, research facilities, student accommodation and public spaces, security and facilities teams are responsible for monitoring a broad range of operational and safety requirements. The information most valuable to those teams is not always linked to the object types a camera was originally trained to recognise.<\/p>\n\n\n\n

Advances in AI-powered CCTV analytics<\/a> are enabling a different approach. Rather than relying solely on predefined object categories, some platforms can now be trained to recognise site-specific objects and behaviours, allowing universities to tailor analytics to their own operational requirements.<\/p>\n\n\n\n

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Why Do Traditional CCTV Analytics Have Limitations?<\/h2>\n\n\n\n

Most video analytics solutions are trained during development to identify common objects such as people, cars, trucks and motorcycles. They may also recognise attributes including colour, direction of travel and vehicle type.<\/p>\n\n\n\n

While these capabilities continue to improve, organisations generally remain limited to the classifications developed by the manufacturer. As a result, universities may have limited ability to create alerts around specific assets, accessibility requirements or operational activities that are relevant to their campus.<\/p>\n\n\n\n

This can make it difficult to apply video analytics to certain use cases without access to more adaptable AI capabilities.<\/p>\n\n\n\n

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How Can AI Analytics Be Trained to Recognise Campus-Specific Objects?<\/h2>\n\n\n\n

New AI analytics platforms can be trained to recognise objects that are specific to a facility and apply custom rules based on those objects.<\/p>\n\n\n\n

One practical example is fire safety equipment. Universities may manage hundreds of fire extinguishers across student accommodation, lecture buildings, laboratories and shared facilities. Once the analytics platform has been trained to recognise a fire extinguisher, alerts can be generated if it is removed, relocated or tampered with.<\/p>\n\n\n\n

This provides facilities and security teams with greater visibility of critical safety assets and may assist with compliance, maintenance and risk management objectives. Alerts can also be integrated into broader security workflows, enabling personnel to investigate issues sooner.<\/p>\n\n\n\n

The same approach could be used to monitor other site-specific assets and activities that would not normally be recognised by conventional video analytics.<\/p>\n\n\n\n

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How Can AI Analytics Support Accessibility Initiatives?<\/h2>\n\n\n\n

Universities continue to invest in creating more accessible environments for students, staff and visitors. AI video analytics can support these initiatives by recognising mobility aids and triggering automated workflows within connected systems.<\/p>\n\n\n\n

For example, an analytics platform trained to recognise wheelchairs could be used to extend door opening times at access-controlled entrances or support automated interactions with connected building systems.<\/p>\n\n\n\n

These types of workflows demonstrate how video analytics can support accessibility initiatives alongside more traditional security objectives.<\/p>\n\n\n\n

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How Can Universities Reduce False Alarms with AI Video Analytics?<\/h2>\n\n\n\n

University campuses generate significant daily activity. Students, staff, contractors, visitors, maintenance teams and delivery vehicles all move through shared environments, often creating activity that may appear unusual to traditional analytics engines.<\/p>\n\n\n\n

Generic detection rules can result in alerts that require investigation despite presenting little operational concern. Over time, excessive nuisance alarms can increase workload and reduce the effectiveness of monitoring programs.<\/p>\n\n\n\n

Custom-trained analytics allow universities to tailor detection rules to their environment. Security teams may choose to:<\/p>\n\n\n\n