Ghost Targets in People-Counting: Why Sensors See Things That Aren’t There

Why some sensors are more prone to ghosts

• Camera systems can misclassify reflections, shadows, or low-light noise as people.
• Radar and ultrasonic devices are susceptible to multipath and reflective surfaces.
• Low-resolution thermal sensors can confuse hot spots with people, though they are generally better at avoiding privacy concerns.

Each sensor type has tradeoffs. The best defense is matching sensor capability to the environment and applying robust signal processing and filtering.

How Butlr’s approach reduces ghost detections

Anonymous heat-based sensing

Butlr uses thermal sensing that captures human heat signatures without capturing identifiable imagery. Heat-based sensing focuses on human thermal contrast rather than visual features, which lowers false positives from visual noise such as shadows or changing lighting.

AI-driven filtering and pattern recognition

Butlr applies machine learning models trained on building-scale data to distinguish human patterns from environmental artifacts. These models consider:

• Temporal patterns: humans move in predictable ways; transient spikes from fans or brief reflections are filtered out.
• Spatial consistency: people occupy physically contiguous pixels in a thermal array; isolated hot pixels are recognized as noise.
• Contextual signals: door events, known HVAC cycles, and adjacent sensors help algorithms decide whether an event is human.

Sensor fusion and networked intelligence

When multiple sensors are deployed, cross-sensor correlation reduces phantom counts. A single spurious reading in one sensor that is not corroborated by neighbors is more likely to be discarded.

Continuous learning and firmware updates

Models and filters are updated as more site-specific data is collected, improving accuracy over time and adapting to seasonal or layout changes.

Practical troubleshooting checklist for ops teams

• Reproduce the problem: Note the time(s) when ghosts occur and check for repeating patterns such as HVAC cycles or weather events.
• Isolate sensors: Temporarily disable or cover individual sensors to identify which device reports the ghost events.
• Inspect the environment: Look for reflective surfaces, heat sources, moving objects, or recent layout changes.
• Check sensor mounting and field of view: Ensure sensors are mounted at recommended heights and angles and that there are no obstructions.
• Review logs and raw data: Examine time-series and heatmap snapshots around ghost events to see if readings are spatially consistent with a human signature.
• Adjust sensitivity and filters: Reduce sensitivity or tighten temporal/spatial thresholds and enable ghost-suppression filters if available.
• Correlate with other systems: Compare sensor events to badge swipes, door contacts, or neighboring sensors to confirm detections.
• Update firmware and models: Apply the latest firmware and model updates for improved ghost handling.
• Conduct a controlled test: Walk test tracks through the sensor’s view and repeat tests with potential confounders present.
• Document and iterate: Record findings, adjustments, and results to refine deployment standards and site-specific rules.

Deployment best practices to prevent ghosts

• Pre-survey spaces: identify heat sources, reflective materials, and high-motion non-human activity before installation.
• Use overlapping coverage: design sensor layouts so events are validated across neighboring sensors.
• Implement staged rollouts: deploy sensors in one area, tune settings, then expand to similar spaces.
• Schedule periodic recalibration: re-evaluate thresholds after HVAC changes, renovations, or seasonal shifts.
• Combine signals when possible: use door sensors or access logs as corroborating evidence for counts.

FAQs

What causes ghost targets most often?

The most frequent culprits are reflections and multipath (for radar/lidar), transient heat sources (for thermal), and environmental motion like HVAC-driven curtains or machinery. Sensor placement and tuning are common root causes.

How can I test if a ghost is caused by multipath or reflections?

Temporarily change the sensor orientation or place a barrier between the sensor and suspected reflective surface. If ghosts disappear, reflections are likely the cause.

Do thermal sensors have fewer false positives than cameras or radar?

Thermal sensors generally avoid visual noise from lighting and privacy concerns from cameras, and they often outperform radar in cluttered indoor environments. However, thermal sensors can still be fooled by hot surfaces or direct sunlight.

Can software fixes eliminate all ghost detections?

No solution guarantees zero false positives. Software filtering and ML significantly reduce ghosts, but careful sensor selection, mounting, and environmental controls are also necessary.

What are quick mitigation steps I can do right now?

Review mounting angles, enable temporal/spatial filtering, correlate events with neighboring sensors, and run controlled walk tests to validate settings.

Next steps for operations and engineering teams

• Start with a focused root-cause assessment using the troubleshooting checklist above.
• If you manage multiple sites, standardize a pre-deployment survey to avoid common pitfalls.
• For teams exploring enterprise-grade occupancy sensing, request a product demo or pilot deployment to evaluate performance in your real environment.

Reducing ghost detections requires good hardware choice, intelligent software, and practical deployment practices to turn noisy occupancy data into reliable insights for operations, energy management, and space planning.

Conclusion

Ghost detections are caused by physical, environmental, and algorithmic factors. Combining anonymous thermal sensing, AI-driven filtering, careful placement, and ongoing tuning significantly reduces false positives and improves the reliability of people-counting analytics.