Does a low-resolution thermal camera find people in darkness and smoke, and what does misalignment cost?

What it found

Synthetic 64×64 scenes with people, cold clutter and warm non-person decoys, seen by a grey RGB camera (shot and read noise, smoke airlight) and a 16×16 thermal camera (0.3 K noise, 10% of the visible smoke extinction assumed). At 10% false alarms per image, RGB alone finds 36% of people in bright clear light, 13% at smoke transmittance 0.4 and none in the dark or dense smoke; thermal alone finds 65% in every condition, capped by the decoys; fusing them gives 94% in bright light and falls back to the thermal level otherwise. A thermal camera displaced by 2 px costs the fused detector 7 points, 4 px (one thermal pixel) 19, and 6 px leaves it below RGB alone (0.12 vs 0.37); training with displacement jitter keeps 0.54 at 6 px but loses 9 points when aligned. Stylised: synthetic scenes, logistic-regression detectors on local contrast, sideways shift only, no real data.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.