By its very nature, a transit area (train station platform, airport concourse, transfer area, waiting area) sees large and fluctuating flows of people. It is precisely this variability that makes human surveillance alone insufficient: an operator cannot simultaneously monitor dozens of screens and detect, within seconds, an abandoned package, an unusual crowd movement, or an intrusion into a restricted area. This guide explains how AI-powered video analytics transforms these areas into controlled and responsive environments.
What is an incident in a transit area?
This term encompasses all abnormal events that can occur in a high-traffic area: unattended luggage or objects, intrusion into a technical or restricted area, a person falling, sudden crowd movement or gathering, or entry into a prohibited area. Each of these events requires a different response, but they all share one thing in common: the faster the detection, the more effective the response and the better the risk is contained.
How video analytics detects these incidents
1. Continuous analysis of the live feed, not passive recording
Unlike traditional video surveillance, which is reviewed after the fact when an incident has already occurred, AI-powered video analytics processes the live feed continuously. Each camera becomes a sensor that interprets the scene in real time rather than simply recording it.
2. Detection rules tailored to each type of risk
- Prolonged stationary object: A piece of luggage or a package left unattended without an identified owner for longer than a configurable time period triggers an alert.
- Intrusion into a restricted area: Any entry into a technical area, a lane, or an unauthorized access point is immediately reported.
- Person on the ground: Fall detection enables rapid intervention, which is particularly critical in high-traffic areas.
- Unusual crowd movement: A sudden gathering or panic movement is identified by a sudden change in density and trajectory within an area.
3. A Contextualized Alert, Not Just a Simple Signal
A useful alert is not a raw notification: it must specify the exact area, the time, and include an anonymized contextual image to enable immediate decision-making by security teams. It is this contextualization that transforms technical data into operational action.
Why GDPR compliance is fundamental in this context
A transit zone is, by definition, a public space traversed by a very large number of people who have not consented to individualized processing. This is why an incident detection solution in this context cannot rely on facial recognition or any other form of biometric processing. Non-biometric approaches process anonymous silhouettes and aggregated movement data: the purpose is never to identify an individual, but to detect risky behavior or situations. This distinction is what makes large-scale deployment legally viable in a public gathering place or a transportation hub.
From Alerts to Actionable Data
Beyond real-time alerts, the history of detected incidents becomes operational data: which areas have the highest concentration of incidents, at what times, and involving what types of events. This retrospective analysis makes it possible to adjust staffing levels, signage placement, or the physical layout of high-risk areas, rather than simply reacting to incidents as they occur.
How CORE by XXII Detects Incidents in Transit Areas
CORE connects to existing camera infrastructure (RTSP streams, virtually all industry standards) without requiring hardware replacement. The platform detects the following in real time:
- Abandoned objects and luggage
- Intrusions into restricted or technical areas
- Falls and prolonged stays on the ground
- Unusual crowd movements and gatherings
- Entry into restricted areas
All of these detections are based on the analysis of anonymous silhouettes, without facial recognition or individual identification, in compliance with the GDPR and the recommendations of the CNIL. Alerts are sent to security teams with precise location data and contextual imagery, enabling a response within seconds rather than minutes.