Beyond Counting — LiDAR That
Tracks, Classifies, and Maps.
Real-time object tracking, dwell time analysis, heat mapping, and spaghetti mapping — all from a single LiDAR deployment. See what’s happening in your space right now, or replay any moment from yesterday.
LiDAR Object Tracking & Heat Mapping — Live Demo
See real-time object classification, dwell time filtering, spaghetti mapping, and zone heat maps in a live system walkthrough.
Live demonstration of LiDAR object tracking, classification, dwell time analysis, and heat mapping
Most people-counting and traffic-monitoring systems answer one question: how many? LiDAR-based object tracking and analytics goes far beyond that — answering who, what type, where exactly, for how long, and in what pattern. The difference between a headcount and full spatial intelligence.
The video above demonstrates exactly what a deployed Smart Sensor Solutions LiDAR system can deliver. Across a fully covered area — with no blind spots, no gaps in detection, and no dependence on lighting conditions — every object in the scene is tracked, classified, and analysed in real time. The same data can be replayed, filtered, and visualised for any previous time window.
This is not a controlled laboratory demonstration. It is a live operational system — the kind deployed at transit hubs, retail environments, public plazas, campus environments, and smart city infrastructure. What you see in the video is what operators and planners actually see on their dashboards, every day.
Traditional counters tell you how many people passed a line. LiDAR spatial analytics tells you where they went, how long they stayed, what type of object they were, and which zones experienced the highest density — continuously, automatically, and without capturing any personal identifying information.
Nine Capabilities. One Deployed System.
The demonstration in the video above showcases the full analytical capability of a Smart Sensor Solutions LiDAR deployment. Here is what each capability delivers and why it matters for real-world operations.
Full-Area Coverage — Zero Blind Spots
The deployment uses an appropriate number of LiDAR units positioned to guarantee true coverage across the entire target area. Every part of the space is monitored — no gaps, no shadows, no uncovered corners regardless of obstacles or layout complexity.
Live Object Tracking & Full Playback
Every object in the scene is tracked in real time with a unique trajectory. Operators can watch live movement or play back tracking for any previous time period — reviewing yesterday’s peak hour, a specific event window, or any historical moment in the dataset.
Object Types & Dwell Times
Each tracked object is classified by type and its dwell time — the duration it remained within the monitored area — is calculated automatically. Dwell time is one of the most operationally valuable metrics for retail, transit, and public space management.
Definable Object Classes
Object classes are fully configurable to your operational context. Pedestrians, cyclists, vehicles, shopping carts, wheelchairs — any object type relevant to your environment can be defined, tracked, and reported on independently.
Dwell Time & Class Filtering
Filter the live view or playback by any combination of dwell time threshold and object class. See only pedestrians who stayed longer than 5 minutes. See only vehicles in a specific zone during peak hours. The filtering is fully flexible and applies to both live and historical views.
Spaghetti Mapping
Spaghetti maps visualise the actual movement paths of all tracked objects as overlaid trajectory lines — revealing the natural flow patterns, desire lines, and movement corridors that emerge organically from real behaviour. Invaluable for space design, wayfinding, and congestion analysis.
Variable Playback Speed
Playback speed is fully adjustable — accelerate to scan a full day in minutes, or slow down to study a specific incident or movement pattern in detail. This makes historical review fast and efficient for operational teams and planners alike.
Zone-Level Heat Mapping
Heat maps show the spatial distribution of object density across the monitored area — revealing which zones attract the most activity, where congestion builds, and how occupancy is distributed across a space over time. Viewable per zone or across all zones simultaneously.
Configurable Time Intervals
The time interval of playback and analysis is fully configurable — view a 15-minute window, an hour, a full day, or a custom date range. All visualisations — tracking, spaghetti maps, heat maps — update to reflect the selected interval instantly.
Object Tracking — What It Reveals That Counting Misses
A people counter at an entrance tells you that 1,200 people entered a space on Tuesday. Object tracking tells you something far more useful: where those 1,200 people went, which zones they visited, how long they stayed in each, and what path they took through the space.
In a transit concourse, this reveals which routes passengers prefer, where queues form before they become a service problem, and whether wayfinding signage is actually directing people where operators intend. In a retail environment, it shows which product areas draw the longest dwell times and which areas people consistently bypass. In a public plaza, it maps the desire lines that reveal where a new pathway would reduce crowding.
The playback capability adds a critical operational dimension. When an incident occurs — a crowd surge, a safety event, an operational bottleneck — the system allows operators to review exactly what happened, when, and how the situation developed. This is not possible with a counting system. It requires tracking.
A transit authority notices complaints about crowding at a platform entrance every weekday between 8:00 and 8:30am. Using object tracking playback, they can replay that exact 30-minute window, observe how and where the queue develops, identify the physical bottleneck, and design an intervention — before the next morning’s peak hour.
Spaghetti Mapping — The Movement Pattern You Never Knew Existed
A spaghetti map overlays the movement trajectories of all tracked objects as lines on a floor plan or spatial diagram. When enough trajectories accumulate — hundreds, thousands, or millions of object paths — patterns emerge that would be invisible to any other form of observation.
Dense clusters of lines reveal the most heavily used corridors and desire paths. Sparse areas show zones that people systematically avoid. Crossing points identify locations where conflicting flows create congestion or safety risk. The map essentially shows you how people actually use your space — which is often quite different from how it was designed to be used.
When filtered by object class, spaghetti mapping becomes even more powerful. Overlaying pedestrian trajectories against cyclist trajectories at an intersection reveals exactly where the two modes interact — and where separated infrastructure would prevent conflict. Filtering by dwell time shows whether high-density corridors are composed of people moving quickly through or lingering in place.
“Spaghetti maps reveal the desire lines that no designer drew but every user follows — the gap between planned space and lived space.”
— Smart Sensor Solutions
Heat Mapping — Density, Dwell, and Zone Intelligence
Heat maps translate the spatial distribution of object activity into an immediately readable visual — warm colours showing high-density zones, cool colours showing low-activity areas. Unlike a spaghetti map which shows individual trajectories, a heat map aggregates activity across any selected time window into a single spatial view.
The system supports heat mapping at two levels. At the individual zone level, operators can examine the density distribution within a specific defined area — useful for understanding how a platform, waiting area, or retail section is actually being used across its footprint. At the full-area level, all zones are displayed simultaneously, making cross-zone comparisons immediate and intuitive.
Heat maps can be filtered by object class and dwell time — revealing not just where density is highest, but what type of objects are creating that density and how long they are staying. A heat map filtered to show only objects with dwell times exceeding 10 minutes tells a completely different story than an unfiltered density map. The former shows you where people linger; the latter shows you where people pass through.
High-Density Zone Identification
Instantly identify which areas of your space experience the highest occupancy — at any time of day, any day of the week. Supports capacity management, staffing allocation, and safety planning.
Dwell Time Heat Maps
Filter the heat map to show zones where objects spend the most time — revealing waiting areas, service queues, popular display positions, and locations where congestion creates extended delays.
Per-Zone or Full-Area View
Analyse a single defined zone in isolation — a platform, an entrance foyer, a retail bay — or view all zones simultaneously for network-level spatial intelligence. Switch between views instantly.
Configurable Time Intervals
Set any time interval for the heat map — 15 minutes, an hour, a day, a full week. Step through intervals to watch density patterns evolve over time, or compare the same interval across different days or weeks.
Where LiDAR Spatial Analytics Delivers the Most Value
The combination of object tracking, spaghetti mapping, and heat mapping is applicable across any environment where understanding how people and objects move through space has operational or planning value.
🚉 Transit Stations & Concourses
Platform crowding analysis, passenger flow routing, queue detection before peak hour, incident playback, and wayfinding effectiveness measurement. Essential for station design and operational safety planning.
🏬 Retail Environments
Dwell time by product zone, traffic flow between departments, checkout queue formation, and footfall pattern analysis. Drives merchandising decisions, staff deployment, and store layout optimisation.
🏟️ Event Venues & Stadiums
Crowd density management, ingress and egress flow analysis, concourse congestion mapping, and emergency evacuation planning. Supports both operational management and post-event analysis.
🏙️ Public Plazas & Streetscapes
Desire line mapping for infrastructure planning, pedestrian volume by zone and time of day, cyclist and pedestrian interaction analysis, and before/after studies for urban design interventions.
🏢 Corporate Campuses & Offices
Space utilisation analysis, meeting area occupancy, cafeteria flow management, and building entry/exit pattern monitoring — all without cameras and in full compliance with workplace privacy requirements.
✈️ Airports & Transport Hubs
Security queue monitoring, gate area density management, retail zone performance, and terminal flow analysis across multiple concourses — with complete historical playback for incident investigation.
Rich Spatial Intelligence — Zero Privacy Compromise
One of the most significant advantages of LiDAR-based spatial analytics over camera-based alternatives is its inherent privacy compliance. LiDAR does not capture images. It captures geometry — the shape, position, and movement of objects in three-dimensional space.
There are no faces. No licence plates. No clothing colours or distinguishing features. No visual data that could identify an individual. The system tracks an anonymous point cloud object from entry to exit, records its dwell time and trajectory, and discards it. What remains is purely spatial and statistical — exactly the intelligence operators need, with none of the privacy exposure that comes with video surveillance.
For public-facing deployments — transit stations, plazas, retail environments — this is not just a legal advantage. It is an operational one. Deploying LiDAR requires no privacy impact assessment for the sensor data itself, no signage requirements for facial recognition, and no data retention policies for personal video footage. The intelligence is rich. The risk is zero.
Everything the System Delivers — In One Deployment
- Full-area coverage with no blind spots — multi-unit deployment scaled to any space size or geometry
- Live object tracking — every object tracked with unique ID, position, velocity, and trajectory in real time
- Full historical playback — replay any previous time window with complete tracking fidelity
- Object classification — definable object classes tracked and reported independently
- Dwell time analytics — automatic calculation of time-in-zone for every tracked object
- Dwell time & class filtering — filter live view or playback by any combination of class and dwell threshold
- Spaghetti mapping — trajectory visualisation with filtering by class and dwell time
- Zone heat mapping — density visualisation per zone or full area, live or historical
- Variable playback speed — accelerate or slow down historical review to any required pace
- Configurable time intervals — set any analysis window from minutes to weeks
- Privacy compliant by design — no images, no personal data, GDPR and PIPEDA compatible
“The question was never whether we could count people. The question was whether we could understand how they actually use the space — and act on that understanding in real time.”
— Smart Sensor Solutions
Ready to See What’s Happening in Your Space?
Whether you manage a transit station, a public plaza, a retail environment, or a campus — we can show you what LiDAR spatial analytics looks like deployed in your context.
