The platform uses deep-learning algorithms to provide a detailed picture of anonymised movement patterns of people for queue and flow analysis.
Veovo, which specialises in data and analytics technology for airports and other public spaces, is introducing 3D camera technology to provide in-depth people queue and flow analysis.
Its BlipVision solution visually counts and tracks end-to-end movements and is designed for use in areas where gaining a detailed picture of people’s movements is difficult.
By using advanced deep-learning algorithms, Veovo said the solution enables individualised, fully anonymised movement patterns, providing a deeper level of flow insight.
The solution, which uses advanced 3D cameras, provides highly detailed views, that help with understanding real-time and predictive queue analysis. These include automatically detecting queue formation, measuring and predicting queue wait times, and live and historical flow visualisation in a web-based user interface.
This allows for the display of wait times in both dynamically forming queues and per unique zone, for instance, load station, counter, buffer zone and more.
BlipVision protects individuals’ identity by only reporting a numerical ID and positions to the system.
BlipVision classifies queues based on their start, via, and end zones, and maintains this classification throughout. Additional insights available include detailed use of assets, such as check-in counters, security lanesand passport control booths.
"By using advanced deep-learning algorithms, the solution enables individualised, fully anonymised movement patterns"
BlipVision can be used to provide per-airline, per-person-class wait times in check-in areas, including areas where counters are dynamically assigned throughout the day.
This is achieved via integration to Veovo’s Airport Operational Database, flight information displays or similar systems.
Veovo’s user interface supports both real-time and historical playback, allowing for real-time situational as well as historical evaluation. This, in turn, allows for ongoing predictive analysis, based on trends and various other factors, making it possible to make decisions before a challenge related to people flows are a reality.
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