Ghost Targets in People Counting: Causes and Mitigation

Why anonymous thermal sensing reduces ghost counts

Anonymous thermal sensing focuses on heat patterns and temporal changes rather than optical features or RF reflections. Key advantages include:

• Lower sensitivity to multipath: Thermal sensors measure emitted or conducted heat and don’t rely on signal reflections, reducing phantom returns.
• Privacy‑preserving: No identifiable imagery, enabling deployments where video is unacceptable and encouraging fuller coverage and better placement for accurate counts.
• Robustness to lighting: Thermal works in all lighting conditions, eliminating sun‑induced false positives that affect cameras.
• Simplified classification: Heat maps and motion vectors make it easier to differentiate human thermal signatures from static heat sources when combined with temporal filtering.

These properties help platforms that use thermal sensing achieve lower false positive rates and more reliable occupancy metrics in many real‑world environments.

Best practices for installation to minimize ghosting

Correct placement and configuration often eliminate most ghost targets before algorithmic fixes are needed.

• Choose the right mounting height and angle: Follow manufacturer guidance to ensure full coverage without overlapping adjacent zones.
• Avoid direct HVAC or sunlight in the sensor field: Place sensors away from vents, large windows, or reflective surfaces when possible.
• Use focused fields of view: Narrow detection zones reduce stray triggers from adjacent areas.
• Control environmental motion: Minimize moving signage, hanging decorations, or oscillating fans near detection paths.
• Calibrate after installation: Let sensors learn the environment during a commissioning period to tune thresholds and filters.
• Pair sensor types where needed: For high‑risk areas, combine anonymous thermal sensing with a secondary modality or door contact sensors for cross‑validation.

Testing and troubleshooting checklist

A structured test plan helps identify and resolve ghost sources.

• Walk tests: Perform controlled walks through the detection zone at different speeds and paths to verify tracking accuracy.
• Static object test: Place non‑human objects (cart, mannequin, box) to confirm they are not counted.
• Day/night validation: Test across lighting conditions and HVAC cycles to see if ghosts correlate with environmental changes.
• Event correlation: Compare sensor logs with known events (badge swipes, turnstile counts) to spot discrepancies.
• Noise capture and analysis: Record raw sensor outputs where possible and look for repeating patterns that indicate mechanical sources or reflections.
• Firmware and algorithm updates: Ensure sensors are running the latest processing stacks; many ghost issues are mitigated by improved filters and models.

Simple algorithmic and operational mitigations

Even after good placement, software tweaks can further reduce ghost counts.

• Temporal filtering: Require consistent persistence of a detection over a short time window before counting.
• Spatial smoothing: Ignore isolated single‑pixel or small‑area blips that do not form human‑scale thermal signatures.
• Motion vector validation: Validate that detected heat signatures exhibit humanlike movement patterns.
• Environmental masking: Configure zones to ignore known static heat sources or reflectors.
• Anomaly detection: Flag sudden unexplained spikes for review instead of automatically triggering actions.

When to escalate to a different solution

Some environments are particularly challenging for any single sensor type. Consider alternatives if issues persist after placement and filtering.

• Persistent ghosting remains after placement and filtering.
• The area has extreme thermal clutter (industrial ovens, open skylights).
• Privacy or compliance constraints require non‑visual sensing but current sensors cannot meet accuracy needs.

In those cases, sensor fusion—combining anonymous thermal sensing with door contacts, floor mats, or other non‑visual detectors—can provide robust counts without sacrificing privacy.

How Butlr approaches ghost mitigation

Butlr’s platform uses anonymous, heat‑based sensors and specialized processing to deliver accurate occupancy and people counting with low false positives. Practical focus areas include:

• Sensor design optimized for stable thermal capture in indoor spaces.
• Edge algorithms that filter transient thermal noise and validate human movement patterns.
• Deployment guidance that reduces environmental interference through thoughtful placement.
• Commissioning and analytics tools to verify performance and iteratively reduce ghost detections.

This combination of hardware, software, and installation best practices targets the common causes of ghosting while maintaining privacy.

Conclusion

Ghost targets undermine confidence in occupancy data, but they are often preventable. Start with correct sensor selection and placement, run systematic tests, and apply temporal and spatial filtering to reduce false positives. Anonymous thermal sensing offers a strong balance of accuracy and privacy and, when paired with good deployment practices, significantly reduces ghost detections in real buildings.

If you’re evaluating people counting solutions, prioritize technologies and vendors that demonstrate low false positive rates, provide commissioning guidance, and offer ongoing algorithm improvements to keep data reliable over time.