  Object detection with multimodal inputs can improve many safety-critical
systems such as autonomous vehicles (AVs). Motivated by AVs that operate in
both day and night, we study multimodal object detection with RGB and thermal
cameras, since the latter provides much stronger object signatures under poor
illumination. We explore strategies for fusing information from different
modalities. Our key contribution is a probabilistic ensembling technique,
ProbEn, a simple non-learned method that fuses together detections from
multi-modalities. We derive ProbEn from Bayes' rule and first principles that
assume conditional independence across modalities. Through probabilistic
marginalization, ProbEn elegantly handles missing modalities when detectors do
not fire on the same object. Importantly, ProbEn also notably improves
multimodal detection even when the conditional independence assumption does not
hold, e.g., fusing outputs from other fusion methods (both off-the-shelf and
trained in-house). We validate ProbEn on two benchmarks containing both aligned
(KAIST) and unaligned (FLIR) multimodal images, showing that ProbEn outperforms
prior work by more than 13% in relative performance!
