Ghost Detections in People‑Counting Sensors

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Accurate people counting is critical for occupancy analytics, space planning, energy control, and safety. Ghost detections are spurious sensor readings that an algorithm interprets as a person or movement when no actual person exists.

What are ghost detections?

Ghost detections can be single false counts, persistent phantom tracks, or intermittent noise that inflates occupancy metrics.

Why they matter

They distort analytics and downstream decisions such as HVAC scheduling or retail conversion metrics.
They erode trust in sensor systems and create extra operational overhead to investigate anomalies.
They can lead to unnecessary interventions, like triggering alerts or adjusting building systems.

Different sensing technologies are vulnerable to different ghost mechanisms. Understanding root causes helps select appropriate mitigation strategies.

Common causes by sensor type

Radar (mmWave) sensors

Multipath reflections: pulses reflecting off walls, glass, or metal return delayed echoes that appear as phantom objects.
Side-lobe and secondary reflections: strong reflectors create false angle or distance estimates.
Environmental motion: moving fans, curtains, or HVAC components generate Doppler signatures misinterpreted as people.

Depth cameras and stereo vision

Reflections and specular surfaces: shiny floors and glass produce erroneous depth readings.
Occlusion and silhouette artifacts: partial views produce fragmented depth blobs that confuse tracking.
Lighting variations: IR interference from sunlight or other sources introduces noise for structured light systems.

Thermal / heat-based sensors

Thermal reflections: hot or reflective surfaces can create misleading heat signatures.
Ambient temperature gradients: HVAC vents or sunlit areas can create ghost hotspots.
Low resolution: coarse thermal imaging can merge nearby people or produce spurious small hot spots.

Vision cameras (RGB)

Shadows and lighting changes: moving shadows can be mistaken for motion.
Reflections in glass or polished floors: mirrored images multiply apparent people.
Background motion: screens, signage, and dynamic displays introduce false motion.

Wireless / BLE / Wi‑Fi sensing

Multipath and signal fluctuation: RF propagation changes create perceived motion.
Device phantom presence: lingering MAC addresses or probe traffic can be interpreted as people.

Robust solutions combine hardware choices, installation practices, and software filtering. Multiple layers of mitigation address transient noise and systematic false positives.

Detection and mitigation strategies

Hardware and installation best practices

Mounting and orientation: place sensors to minimize direct view of reflective surfaces and discourage multipath; adjust elevation and angle to remove reflection paths.
Coverage planning: avoid HVAC vents, rotating equipment, or screens that produce repeated environmental motion.
Choose the right sensor: match technology to space—anonymous heat-based sensors often avoid optical and reflective issues that affect cameras and depth sensors.

Signal preprocessing

Background subtraction: maintain a slowly updating model of the static scene and filter out persistent non-human heat or motion sources.
Elevation gating: discard detections outside expected human height ranges when elevation data is available.
Temporal filtering: require minimum duration or continuity for detections to be counted to debounce short spikes.

Tracking and model-based filters

Multi-frame tracking: validate tracks over time and discard short-lived tracks without human-like motion.
Motion and trajectory heuristics: apply speed and acceleration thresholds consistent with human movement.
Confidence scoring: combine size, persistence, and motion pattern into a confidence score tuned per environment.

Sensor fusion

Combine complementary sensors: fuse thermal + radar or depth + passive infrared to leverage strengths of each modality.
Cross-validation: require corroboration from two independent sensors before declaring occupancy in sensitive applications.

Regular calibration and monitoring

Periodic recalibration: update background models after layout changes to prevent new ghost sources.
Health checks and analytics: monitor for sudden shifts in baseline counts and investigate environmental changes or sensor drift.

A mid-size office experienced persistent false counts near a glazed partition and an HVAC diffuser. A combined approach of site survey, hardware changes, software tuning, and adaptive modeling was applied.

Case study: reducing ghost targets in an open-office deployment

Steps taken

Site survey: identified multipath-prone geometry with glass and a low ceiling over the diffuser.
Hardware adjustment: sensors remounted higher and angled to reduce direct reflections into the field of view.
Software tuning: applied elevation gating and required a 2-second continuity threshold for new detections.
Background update: enabled adaptive background modeling to learn the diffuser's thermal signature.

Results

False positives dropped by over 85% and event logs showed improved correlation with badge entries and manual head counts. Combining installation changes, software filters, and adaptive models reduced ghosts without sacrificing sensitivity to real occupancy.

Sensor selection affects both accuracy and privacy. No single technology is perfect; matching strengths to installation challenges is key.

Why sensor choice matters for privacy and reliability

Camera-based systems provide high granularity but raise privacy concerns and are susceptible to optical artifacts. Radar offers good motion sensitivity but can suffer multipath ghosts. Heat-based sensing provides anonymous occupancy detection using thermal signatures.

Advantages of heat-based, anonymous sensors

Reduce privacy risk because they do not capture facial or identifiable visual imagery.
Less sensitive to visual lighting and reflections that plague cameras.
Often produce lower false positives in reflective or visually noisy environments, though they require careful handling of thermal gradients.

Best results often come from combining modalities so one sensor compensates for another's weaknesses.

Use this checklist during planning and commissioning to reduce false positives and improve system reliability.

Deployment checklist to minimize ghost detections

Conduct a site survey to identify reflective surfaces, HVAC outlets, and moving equipment.
Choose sensor mounting height and angle to avoid direct reflections and limit sightlines to noisy areas.
Apply elevation gating and minimum-duration thresholds in software configuration.
Enable background subtraction and adaptive models that learn static environmental signatures.
Use multi-sensor fusion in high-criticality areas where false positives have high operational cost.
Schedule regular recalibration after layout or seasonal changes.
Monitor baseline metrics for sudden shifts and investigate quickly.

Common questions about identifying and reducing ghost detections.

FAQs

How can I tell a ghost detection from a real person?

Ghosts are often short-lived, lack coherent motion, repeat at fixed locations, or correlate with environmental events like HVAC cycles. Cross-check with other sensors or manual observation to confirm.

Can firmware or software fixes eliminate ghosts completely?

They can dramatically reduce ghosts but may not eliminate all, especially those caused by complex multipath or physical reflections. Combining installation, preprocessing, tracking, and fusion delivers the best results.

Which sensor types are most susceptible to ghost detections?

Depth cameras and optical systems are sensitive to reflections and lighting; radar is prone to multipath. Thermal and wireless sensors have unique causes but can offer privacy and environmental advantages.

Should I use multiple sensor types?

For critical spaces where accuracy is essential, combining sensor types and using fusion strategies typically yields the most reliable outcome.

Start deployments with a thorough site assessment and tune both hardware and software to the environment. Favor layered mitigation including proper mounting, preprocessing, tracking heuristics, and sensor fusion where needed.

Final recommendations

For privacy-sensitive or reflection-prone spaces, consider anonymous heat-based sensing to balance reliability and anonymity. If ghost detections affect analytics or operations, schedule a diagnostic assessment with a sensor specialist to identify a small set of actionable changes that yield large improvements.

Ghost Detections in People‑Counting Sensors: Causes, Mitigation, and Best Practices