Reducing Ghost Targets in People-Counting Sensors — Radar vs Thermal Approaches

How radar and depth sensors produce ghost targets

Radar and depth sensors (including some LIDAR and stereo camera systems) are popular for people-counting because they detect motion and distance directly. But each has vulnerabilities:

Radar sensors

• Strengths: Good range and motion detection, works in low light.
• Weaknesses: Prone to multipath reflections and specular returns, which create ghost targets. Signals can bounce off shiny or metallic surfaces and appear as phantom people. Radar also struggles with differentiating stationary people from background if algorithms rely on movement.

Depth sensors and stereo cameras

• Strengths: Provide 3D information and can distinguish between objects and people by shape.
• Weaknesses: Sensitive to occlusion and fragmentation when people cluster or overlap; reflective surfaces and glass can confuse depth calculations. Performance can degrade in bright sunlight or when dust and fog are present.

Both sensor classes can benefit from sensor fusion and advanced processing, but they often require more calibration and site-specific tuning to suppress ghosting effectively.

Why heat-based (thermal) sensing produces fewer false targets

Thermal sensing detects infrared radiation (heat) emitted by bodies rather than relying on reflected signals. This fundamental difference reduces several ghosting mechanisms:

• Less multipath: Thermal energy is emitted rather than reflected, so ghosting due to signal bouncing is far less common.
• Direct human signature: People produce a consistent heat signature that is easier to separate from inanimate objects.
• Privacy-preserving: Thermal imagery is low-resolution and does not capture identifying visual details, making it suitable for occupant analytics without video privacy concerns.

Thermal sensors still face challenges, such as differentiating hot equipment from people or detecting stationary individuals whose thermal contrast is reduced. However, combined with smart algorithms, thermal systems tend to produce fewer and more interpretable false positives compared with radar in many indoor settings.

Butlr’s approach to minimizing ghost targets

Butlr combines heat-based hardware with software processing engineered to reduce ghost counts and improve reliability:

• Anonymous, heat-based sensors: The hardware senses thermal signatures rather than visible imagery, minimizing reflections and visual privacy concerns.
• Multi-sensor deployment options: Wired and wireless sensors can be placed to reduce occlusion and blind spots while providing overlapping coverage that improves consistency.
• Algorithmic filtering: Temporal smoothing, object persistence logic, and scene-adaptive thresholds reduce transient blips and duplicate detections.
• Calibration and tuning: Site-specific calibration takes into account room geometry, HVAC flows, and typical occupant behavior for lower false positive rates.
• Integration with analytics: The platform provides post-processing that aggregates counts into usable occupancy metrics, flagging anomalous spikes that may indicate ghosting for review.

This combination of physics (thermal sensing) and software yields robust occupancy data suitable for HVAC control, space utilization, and safety monitoring.

Practical deployment and calibration tips to reduce ghosting

Good installation and configuration are as important as sensor choice. Use these practical tips to minimize ghost targets on any platform:

Plan sensor placement

• Avoid pointing sensors directly at large reflective surfaces like windows or polished metal.
• Position sensors to get an unobstructed view of ingress/egress paths rather than broad open areas where occlusion and clustering occur.

Use overlapping coverage

• Two sensors with partial overlap reduce ambiguity: if both agree, the detection is likely real; if they disagree, software can apply additional checks.

Calibrate for the environment

• Account for HVAC vents, equipment heat sources, and typical furniture placement during setup.
• Run an initial calibration period and review logs to tune detection thresholds.

Enable temporal and spatial smoothing

• Require object persistence for short durations before incrementing counts. This reduces transient blips from non-human motion.

Regularly review and update

• Periodically audit unusual spikes or persistent discrepancies and adjust sensor orientation or algorithm parameters as needed.

Combine sensor types where appropriate

• In challenging spaces, fusing thermal data with another modality (e.g., passive infrared or doorway switch) can provide redundancy and higher confidence.

Example results and use cases (high level)

Organizations that switch to or deploy thermal-based occupancy sensing typically see:

• Reduced false positive rates in spaces with high reflective surfaces compared to radar-only systems.
• Improved privacy compliance in public-facing or sensitive environments, due to non-identifying thermal data.
• Reliable continuous occupancy trends for HVAC optimization and space utilization without invasive camera footage.

These outcomes depend on proper deployment and ongoing monitoring, but the thermal approach simplifies many common ghosting problems encountered with reflection-prone sensors.

FAQs

What causes ghost counts in people-counting systems?

Ghosts arise from reflections, environmental motion, sensor noise, occlusion, and poorly tuned detection thresholds. Each sensor type is susceptible to different combinations of these causes.

How does Butlr handle occlusion and multiple people near each other?

Through deployment best practices and algorithms that use temporal persistence and spatial context, Butlr reduces fragmentation and merges detections that represent one person moving through the scene.

Can thermal sensors detect stationary people?

Yes. Thermal sensors detect heat signatures rather than motion, so with appropriate sensitivity and calibration they can register stationary occupants, though contrast can vary with ambient temperature and clothing.

Are there situations where radar or depth sensors are better?

Radar and depth sensors can excel in long-range detection or outdoors where thermal contrast is limited. The ideal choice depends on the environment and the kinds of errors you can tolerate.

Next steps for facilities and engineering teams

• Conduct a site assessment: Identify reflective surfaces, HVAC sources, and high-traffic paths that influence sensor performance.
• Pilot before roll-out: Test a small set of sensors in representative locations to validate false positive rates and tuning needs.
• Monitor and iterate: Use dashboards and logs to spot ghosting patterns and adjust placement or algorithm settings.
• Consider privacy and compliance: If visual privacy is a concern, thermal sensing offers an inherently anonymous approach.

If you’re evaluating people-counting solutions for occupancy analytics, HVAC optimization, or space planning, prioritize a combination of sensor physics, deployment strategy, and algorithm robustness. Heat-based sensing, combined with thoughtful calibration and processing, reduces many common sources of ghost targets and delivers reliable, privacy-preserving occupancy data for operational use.

For teams interested in piloting heat-based occupancy sensing or comparing accuracy across sensor types, request a demo or a site validation to see real-world performance in your environment.