Veovo Introduces 3D Camera Technology for In-Depth People Queue and Flow Analysis

In situations where queue mazes are flexible and changeable, for instance in airports, gaining an accurate, detailed picture of people´s movement patterns can be a challenging task. To combat this, Veovo has launched its new BlipVision solution, which visually counts and tracks end-to-end movements and minimizes bindings to the queue layout.

By using advanced deep-learning algorithms, the solution enables individualized, 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 visualization 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 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 lanes, passport control booths etc.

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 and FIDS solutions or similar systems.

• Visually counts and tracks people’s end-to-end movements
• Individualised, fully anonymised movement patterns, displayed in a web-based user interface
• Provides wait times in both dynamically forming queues and per unique zone

BlipVision protects individuals’ identity by only reporting a numerical ID and positions to the system.

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.