People Flow Monitoring 2.0: How LiDAR solutions outperform other technologies

Frequently Asked Questions

• What is the difference between people counting and people flow monitoring?

  People counting produces a single integer: how many individuals crossed a line or occupied a zone. People flow monitoring produces continuous trajectories, with each person tracked from entry to exit and attributes such as speed, direction, dwell time, and queue membership recorded frame by frame under a persistent anonymous ID. The distinction matters operationally because a count tells a manager how busy a checkpoint is, while a flow trace reveals why it slowed down, where upstream congestion started, and whether the same delay recurs at a structural level. Outsight's Motional Digital Twin is built around this second, richer layer, generating real-time anonymous 3D trajectories across sites like Paris-Charles de Gaulle airport and SNCF train stations to support exactly that kind of structural flow analysis.

• Can a LiDAR people-monitoring system work in venues with very low ceilings?

  Low ceilings are actually advantageous for LiDAR coverage. A single ceiling-mounted sensor at 3 to 4 meters height can cover a wide floor area because the sensor's field of view sweeps a large horizontal cone close to the ground. Outsight's infrastructure-based approach, used in deployments across train stations operated by SNCF Gares & Connexions in France, relies on exactly this geometry: sensors mounted in the infrastructure overhead capture precise 3D shape and motion data across dense crowd environments without any loss of accuracy at reduced heights. In contrast, camera-based systems at low ceiling heights suffer from severe perspective distortion and occlusion when crowds are dense, requiring many more units per square meter to achieve comparable coverage.

• Does LiDAR people monitoring meet GDPR requirements without additional configuration?

  LiDAR sensors emit laser pulses and measure the geometry of reflected surfaces. The resulting 3D point cloud encodes shape, position, and motion but contains no pixel data, so faces, skin color, and identifying features are physically absent from the raw capture. Because no personal data is recorded in the first place, there is no anonymization step required to achieve GDPR compliance: the system satisfies the regulation by the nature of the sensor, not by a post-processing filter that could be misconfigured or disabled. Outsight describes this property as "anonymous by definition," a core characteristic of its infrastructure-based LiDAR deployments at sites such as Paris-Charles de Gaulle and SNCF train stations, where the Motional Digital Twin tracks people flow without ever capturing biometric data.

• How does sensor fusion work when multiple LiDAR units cover the same space?

  Each sensor produces its own 3D point cloud referenced to a local coordinate frame. A software layer re-projects every sensor's output into a single shared coordinate system, then merges the overlapping point clouds into one unified scene. Objects that appear in two sensors' fields of view are deduplicated rather than double-counted. Outsight's SHIFT platform applies this fusion logic across multi-vendor LiDAR hardware (including Hesai, RoboSense, and Ouster units) within a sub-50ms end-to-end pipeline, so the merged scene stays current in real time. The result is that a person walking from one sensor's zone into another carries the same anonymous tracking ID continuously, with no gap or handoff artifact in the data stream.

• What happens to people-monitoring accuracy when a tracked person stops moving for an extended period?

  Stationary objects are among the more demanding edge cases in 3D perception pipelines. A person standing still produces a static point cluster that can be misclassified as furniture or infrastructure between frames. Robust software addresses this by maintaining the entity's ID and bounding box across a configurable timeout window, cross-referencing the cluster's shape signature against the known floor plan, and reactivating the trajectory when motion resumes. Outsight's Motional Digital Twin handles exactly this scenario by processing point cloud geometry in real time, at sub-50ms latency, so a motionless person retains a persistent identity rather than being dropped from the scene model. Dwell-time KPIs depend directly on this capability, because the system must distinguish a person standing in a queue from a fixed object to produce reliable occupancy and flow analytics.

• What output format does a LiDAR people-monitoring platform expose for downstream integration?

  After the 3D perception layer classifies and tracks entities, the data is typically surfaced through a documented API as a structured event stream: per-entity records carrying a timestamped position, bounding box, classification label, and behavioral state. Outsight's SHIFT platform follows this model, exposing open integrations so operators can connect the Motional Digital Twin to existing operations platforms without proprietary lock-in. Aggregated KPIs (zone occupancy counts, queue lengths, average dwell times) are delivered separately on configurable intervals, ranging from sub-second for live alerting to hourly or daily for analytics dashboards. Standard integration targets include REST endpoints, MQTT brokers, and WebSocket connections, covering the control-room displays, BMS systems, and data warehouses that airport and transit operators typically run in parallel.