Ghost Detections in People-Counting Systems — Causes and How Butlr Prevents Them

Common root causes of ghost detections

• Multipath and reflections: Signals bounce and return from non-human surfaces.
• Environmental noise: HVAC vents, sunlight, hot equipment, or rapid temperature shifts.
• Sensor placement and overlap: Poor positioning causes sensors to view the same subject multiple ways.
• Low-quality filtering or simplistic algorithms: Too-sensitive thresholds or lack of temporal smoothing.
• Calibration drift and hardware noise: Aging devices or wireless interference can add spurious readings.

How Butlr’s approach minimizes ghost detections

Butlr combines anonymous, heat-based sensing with edge and cloud AI to reduce false detections without capturing images or personal data. Key elements of the approach include:

Heat-based sensing principle

Sensors detect thermal signatures rather than visible images, making them less sensitive to optical reflections and lighting changes.

Multi-point, low-resolution thermal arrays

These capture occupancy patterns while preserving anonymity and reducing data dimensionality for robust analysis.

On-device preprocessing

Edge filtering reduces raw sensor noise and prevents transient artifacts from reaching analytics pipelines.

AI-driven temporal and spatial filtering

Models analyze patterns over time and across sensors to distinguish humans from transient heat events or reflections.

Sensor fusion and redundancy management

When multiple sensors overlap, algorithms reconcile readings to avoid duplicate counts and compensate for occlusions.

Together, these techniques deliver high accuracy in real-world conditions, especially in spaces where optical or radar solutions struggle.

Practical mitigation checklist for reducing ghosts

• Start with placement and orientation: Avoid aiming sensors directly at reflective glass, shiny surfaces, or active HVAC vents.
• Use multiple sensors strategically: Overlapping coverage should be managed by software-based fusion to remove duplicates.
• Implement temporal smoothing: Ignore extremely short detections and require minimal dwell time before logging a count.
• Calibrate for the environment: Adjust sensitivity for rooms with heat-generating equipment or strong sunlight.
• Monitor and log diagnostics: Track false-positive patterns to tune thresholds and update models.
• Run pilot deployments before scaling: A short pilot reveals local sources of ghosting and supplies site-specific tuning data.

Deployment tips to prevent duplicates and improve accuracy

• Define sensor zones with clear overlaps and blending rules so a person moving between sensors isn’t counted twice.
• Use adaptive thresholds that adjust for ambient temperature shifts over the day.
• Integrate with building systems to provide context (e.g., HVAC state) that can explain transient signals.
• Schedule periodic recalibration and firmware updates to keep filtering and model weights current.

Real-world examples (summarized)

Office floor with reflective atrium glass

Challenge: Depth cameras reported phantom people near glass; thermal sensors from Butlr avoided reflections and delivered stable counts.

Gym with moving equipment and warm surfaces

Challenge: Heated machinery and rapid motion created spurious radar returns; heat-based sensing plus temporal filters filtered out non-human heat sources.

Lecture hall with clustered seating

Challenge: Overlapping sensors double-counted ingress/egress; software fusion reconciled overlaps and produced accurate utilization metrics.

These examples show common scenarios where choice of sensor modality and analytic approach makes a measurable difference.

Frequently asked questions

Are thermal sensors immune to ghost detections?

No sensor is completely immune, but heat-based sensors are less susceptible to optical reflections and many types of multipath. Combining thermal sensing with AI and site-aware tuning greatly reduces ghost rates.

Can HVAC vents or equipment create false thermal targets?

Yes. Strong, localized heat sources can be mistaken for people without appropriate filtering. Mitigation includes placement changes, temporal smoothing, and specialized model training.

How do you avoid double-counting people moving between sensors?

Use sensor fusion rules and spatial-temporal analytics that recognize movement continuity and assign a single track to a person across adjacent zones.

Does this approach capture identifiable images or personal data?

Butlr’s heat-based sensors are anonymous by design. They do not capture visual imagery and are built to preserve privacy while providing occupancy insights.

How do I validate detection accuracy on my site?

Run a short pilot with ground-truth logging (manual counts or temporary cameras for validation only), evaluate false positives/negatives, and tune placement and filters before full deployment.

Closing summary and next steps

Ghost detections are a common, solvable problem in people-counting systems. The right combination of sensor modality, placement, edge filtering, and AI-driven temporal/spatial analytics dramatically reduces false positives. Butlr’s anonymous heat-based sensing approach focuses on real-world robustness and privacy, helping buildings get reliable occupancy data without the pitfalls of optical or radar ghosting.

Next steps: Pilot a privacy-first heat-based sensing solution in representative spaces, use diagnostic logs and short-term validation to tune placement and models, and prioritize vendors that combine robust hardware, on-device filtering, and AI-driven analytics. Contact Butlr to request a pilot, review sample accuracy reports, or arrange a site assessment with recommendations tailored to your building's challenges.