Eliminating Ghost Detections in People-Counting Systems

Deployment checklist to avoid false positives

Follow this checklist during planning and installation to reduce ghost detections from the start.

• Conduct a site survey: Walk the space at different times and note potential heat sources, reflective materials, and high-traffic pathways.
• Choose mounting height and angle carefully: Follow vendor guidelines to ensure the sensor’s field of view covers intended areas without including HVAC grills or windows.
• Avoid direct sunlight and vents: Place sensors away from windows or diffusers that create transient thermal events.
• Define zones explicitly: Configure the sensor to ignore irrelevant sub-areas such as appliance zones or decorative heaters.
• Use overlapping coverage where needed: Two sensors with overlapping fields increase confidence via correlation and reduce single-sensor errors.
• Establish a baseline profile: Capture background thermal patterns for several days to inform adaptive baselining.
• Run an acceptance test: Simulate typical occupancy patterns and measure false positive rates before handover.
• Schedule firmware and model updates: Keep edge and cloud models current to benefit from improved filtering and bug fixes.
• Create a maintenance plan: Include periodic re-calibration and a quarterly review of false-positive trends.

Troubleshooting: step-by-step approach

If you observe ghost detections, follow a structured process to diagnose and remediate the issue.

• 1. Reproduce the event: Note time, location, and environmental conditions when ghost detections occur and try to recreate the scenario.
• 2. Check raw sensor outputs: Look at low-level thermal data to see whether the input is noisy or whether the algorithm misinterprets a clear human-shaped reading.
• 3. Inspect the environment: Identify nearby heat sources, reflections, or changes such as new equipment or furniture.
• 4. Validate placement: Confirm mounting height and angle have not shifted and that the sensor’s field of view is unobstructed.
• 5. Apply temporal filters: Increase dwell thresholds or smoothing windows and observe whether false positives drop.
• 6. Use multi-sensor correlation: Temporarily enable overlapping sensors or cross-check with a second modality to validate events.
• 7. Update models and firmware: Apply recommended updates and re-run acceptance tests.
• 8. Iterate and log results: Track changes, measure improvements, and document remaining edge cases for future model training.

Measurement and validation best practices

Reliable metrics and test designs are essential for proving improvements and maintaining trust.

• Use ground-truth observations: Compare sensor outputs against human counts or video used only for validation if privacy rules permit.
• Define clear metrics: Track false positive rate, false negative rate, precision, recall, and overall accuracy.
• Test diverse scenarios: Validate in crowded conditions, near HVAC cycles, during peak sunlight, and across weekdays and weekends.
• Keep a log of environmental changes: Note any furniture moves, new equipment, or schedule changes that could affect thermal signatures.
• Export and analyze data: Regularly review exported occupancy logs to identify recurring patterns of ghost detections and prioritize fixes.

Common questions (FAQ)

Q: What causes ghost detections in people-counting systems?

A: Ghost detections arise from environmental thermal sources, reflections, incorrect sensor placement, hardware noise, and algorithmic limitations like fixed thresholding or lack of temporal filtering. Both sensor and software adjustments are usually needed to fix them.

Q: How accurate are thermal sensors in crowded environments?

A: Modern thermal people-counting systems can perform well in crowds when they combine array sensing with AI that models density and motion. Accuracy depends on sensor resolution, placement, and the sophistication of filtering and fusion algorithms.

Q: Can sensor placement reduce false targets?

A: Yes. Proper mounting height, avoiding vents and windows, and defining sensing zones are among the most effective steps to minimize false positives. Overlapping coverage and a thorough site survey further reduce risk.

Next steps and CTAs

Reliable occupancy analytics start with careful planning and sensors that are built for real-world environments. To move from troubleshooting to long-term reliability consider these next steps:

• Request a reliability report tailored to your space to identify likely ghost sources.
• Run a pilot with adaptive thermal sensors and AI filtering to measure real-world improvements.
• Request a demo to see live filtering, data export, and how anonymous thermal sensing preserves privacy while improving accuracy.

With the right thermal sensing approach and deployment discipline, you can dramatically reduce false positives and unlock dependable occupancy analytics for HVAC, scheduling, and safety use cases.

Addressing ghost detections requires a blend of good hardware placement, adaptive algorithms, and ongoing validation. With considered deployment and AI-driven thermal sensing, organizations can dramatically reduce false positives and gain dependable occupancy analytics for HVAC, scheduling, and safety.