What Are "Ghost" Targets in People‑Counting Sensors — Causes & Fixes

Detection and mitigation techniques (software and hardware)

Physical and installation fixes

• Reconsider placement: Move sensors away from reflective surfaces, HVAC outlets, or direct sunlight. Small changes in angle often reduce multipath and reflections.
• Add baffles or shields: For radar and thermal installations, passive barriers block stray paths and reduce side lobe detections without obstructing coverage.
• Secure mounting: Vibration or unstable mounts create spurious motion signals; tighten and isolate mounts where possible.
• Environment control: Reduce moving heat sources or shield cameras from bright light changes if feasible.

Software filtering and tracking

• Temporal smoothing: Apply short‑window smoothing to reject single‑frame blips while preserving real motion.
• Spatial clustering: Require minimal spatial persistence or cluster size before reporting a detection as a person.
• Velocity and trajectory checks: Discard detections exhibiting impossible speeds, accelerations, or inconsistent trajectories.
• Elevation gating: For 3D sensors, filter out returns at heights unlikely for people (e.g., floor reflections or ceiling echoes).
• Confidence thresholds: Use sensor confidence scores to reduce low‑quality detections; tune thresholds conservatively to balance recall and precision.

Multi‑sensor fusion

• Cross‑validate: Only report an occupancy event when at least two modalities (for example thermal + radar) agree within time and space windows.
• Weighted fusion: Assign greater trust to sensors less affected by local artifacts and adjust fusion weights based on environmental context.
• Time alignment: Ensure tight timestamp synchronization to avoid false tracks caused by latency differences.

Data quality monitoring and adaptive tuning

• Drift detection: Monitor baseline noise and auto‑recalibrate thresholds when long‑term shifts occur (temperature seasons, new equipment).
• Automated anomaly detection: Flag sudden spikes in false positives and trigger a maintenance checklist for operators.
• Continuous learning: Use labeled edge cases from field teams to improve classification models and reduce repeat ghosts.

Step‑by‑step troubleshooting checklist

• 1. Reproduce the ghost: Note time, location, and conditions (lights, HVAC, doors).
• 2. Inspect the environment: Look for reflective surfaces, vents, or new equipment near the sensor.
• 3. Check mounting and cabling: Ensure the sensor is stable and secure.
• 4. Review raw sensor data: Is the artifact coherent (a track) or isolated blips? This guides filtering choices.
• 5. Test simple filters: Apply temporal/spatial thresholds to see if the pattern disappears without harming true detections.
• 6. Try repositioning: Move the sensor a few degrees or relocate to test for multipath reduction.
• 7. Introduce corroboration: If possible, enable multi‑sensor fusion and verify whether the ghost persists.
• 8. Log and iterate: Record outcomes and adjust confidence/thresholds gradually to preserve detection sensitivity.

Butlr’s approach: reducing ghosts while preserving privacy

Butlr focuses on anonymous, thermal‑based people sensing that is inherently privacy friendly because it doesn’t capture imagery or personal identifiers. To reduce ghost targets in real deployments, a layered approach is used:

• Hardware and placement guidance minimizes environmental heat artifacts and optimizes sensor field of view.
• Specialized signal processing and adaptive filtering remove transient heat blobs and small nonhuman heat sources.
• Multi‑sensor strategies and occupancy models corroborate events across time and space to suppress persistent phantom tracks.

Field deployments typically combine tailored filters with continuous monitoring to maintain high reliability as environments change.

Quick rules of thumb for operators

• If ghosts are occasional: Start with temporal smoothing and a small increase in confidence thresholds.
• If ghosts are persistent in one location: Inspect for reflective or heat sources and try repositioning or adding a barrier.
• If ghosts fluctuate with time of day: Look at sunlight/lighting schedules and HVAC cycles—add scheduled calibration or adaptive thresholds.
• Use multi‑sensor corroboration when accuracy is critical, especially in high‑traffic or safety‑sensitive areas.

FAQs

Can software alone fix all ghost target problems?

Not always. Software can mitigate many artifacts, but persistent environmental causes (reflections, direct sunlight, HVAC vents) often require physical changes like repositioning or shielding.

When should I consider replacing a sensor?

Replace hardware when the unit shows signs of degradation (sensor drift beyond calibration limits), or when the sensing modality is fundamentally unsuited to the environment despite mitigation efforts.

How do I know if ghost targets are caused by multipath or by small moving objects?

Multipath typically creates repeatable, location‑dependent ghosts (often mirroring real paths). Small moving objects produce irregular, low‑confidence blips. Inspect raw data and correlate with environmental observations.

Will multi‑sensor fusion always reduce ghosts?

Fusion reduces ghosts when sensors are complementary and well‑synchronized. Poor registration or misaligned fields of view can introduce new artifacts, so fusion must be implemented carefully.

Conclusion

Ghost targets are a solvable problem when approached systematically. Combine good installation practices, targeted software filters, and sensor fusion to dramatically reduce false detections. For teams deploying occupancy sensing at scale, adopt continuous monitoring and iterative tuning so systems remain reliable as spaces and usage patterns evolve.

If false positives persist after following these steps, engaging a sensor specialist to audit placement, calibration, and fusion strategies is the next practical step.

Conclusion

Ghost targets are a solvable problem when approached systematically. Combine good installation practices, targeted software filters, and sensor fusion to dramatically reduce false detections. For teams deploying occupancy sensing at scale, adopt continuous monitoring and iterative tuning so systems remain reliable as spaces and usage patterns evolve. If false positives persist after following these steps, engaging a sensor specialist to audit placement, calibration, and fusion strategies is the next practical step.