Understanding Ghost Targets in People‑Counting Systems

Real-world examples

• A radar sensor near a reflective metal column reports a continuous occupant in an adjacent corridor
• A camera-based counter misclassifies a poster with a human silhouette as a person under certain lighting
• A thermal node shows intermittent detections near a ceiling vent on warm days, inflating occupancy counts in a meeting room

Why ghost detections matter

Ghosts are more than a nuisance; they have operational and financial consequences that undermine the value of sensor deployments.

• Incorrect analytics: overstated or unstable occupancy metrics mislead space planners
• Faulty automation: HVAC, lighting, and access controls may behave erratically when triggered by false counts
• Wasted energy and cost: spaces may be heated, cooled, or lit unnecessarily
• Loss of trust: facility teams and stakeholders may stop relying on sensor data

Mitigation strategies — practical approaches

Reducing ghost targets requires a layered strategy: better sensors, smarter processing, and careful installation.

1. Choose the right sensing modality

• Match sensor physics to the environment; thermal sensors measure emitted heat and are less prone to multipath reflections than radar or ultrasonic systems
• Consider privacy: cameras capture identifiable imagery, while anonymous thermal sensing preserves privacy

2. Improve sensor installation and placement

• Avoid reflective surfaces in the direct field of view and mount sensors to minimize reflections and oblique angles
• Use appropriate mounting heights and aim angles to limit coverage to intended detection zones
• Verify coverage overlap carefully when using multiple sensors to prevent double counting

3. Apply signal processing and filtering

• Background subtraction: continuously model and remove stationary thermal or radar backgrounds
• Temporal smoothing and gating: require a minimum presence duration before confirming a count
• Spatial clustering: group nearby detections into single tracks to reduce duplicate counts

4. Use machine learning and tracking consistency

• Track continuity checks: use velocity and path consistency to discard sudden implausible jumps
• Classification models: train models to distinguish people from common false-positive sources
• Anomaly detection: flag and review unusual patterns such as persistent single detections

5. Sensor fusion where appropriate

• Combine complementary sensors (for example thermal plus low-resolution presence sensors) to require corroboration
• Design fusion rules conservatively so false positives from one modality are vetoed by another

6. Ongoing calibration and validation

• Periodic recalibration to account for environmental and seasonal changes
• Collect validation datasets under different conditions to tune thresholds and retrain models

Why anonymous thermal sensing reduces ghosts

Heat-based sensors detect emitted infrared energy rather than reflections of emitted signals, which provides practical advantages for reducing false detections.

• Fewer multipath artifacts: thermal energy does not reflect like radar, so false returns from distant reflectors are far less likely
• Robust in varying lighting: thermal works in darkness and bright light without visual artifacts that confuse cameras
• Privacy preserving: thermal imagery can detect presence and counts without identifying individuals

When combined with AI-driven analytics, thermal sensing can reduce false positives from HVAC, furniture, and other thermal noise through learned background models and pattern recognition.

Deployment checklist for reducing ghost detections

• Inspect mounting locations for reflective surfaces and heat sources
• Select sensors suited to the environment (thermal for indoor occupancy, radar for large open nonreflective spaces)
• Configure minimum dwell times and track consistency requirements
• Set up periodic calibration and automated drift detection
• Create a validation plan with ground truth collection under different conditions
• Log and review anomalous detections and tune models accordingly

Testing and validation plan (quick steps)

• Baseline test: record occupancy in a controlled scenario with known counts for several hours
• Stress test: simulate edge cases — HVAC cycles, sun angles, reflective objects, and multiple people crossing
• Long-term monitoring: compare sensor counts against booking systems, manual audits, or CO2 trends over weeks
• Iteration: use mismatches to adjust thresholds, retrain classifiers, or change sensor placement

Conclusion — practical next steps

Ghost targets are a common but solvable problem in people-counting systems. Start with the right sensor for your environment, follow a careful installation plan, and apply layered software mitigations including tracking consistency and classification.

If you manage spaces and see persistent ghost detections, collect a short set of ground-truth samples (examples of false positives and expected behavior) and use them to guide placement, configuration, or model retraining. Vendors with AI-driven, privacy-preserving platforms can often help accelerate testing and reduce false detections in production deployments.