  Mistakes/uncertainties in object detection could lead to catastrophes when
deploying robots in the real world. In this paper, we measure the uncertainties
of object localization to minimize this kind of risk. Uncertainties emerge upon
challenging cases like occlusion. The bounding box borders of an occluded
object can have multiple plausible configurations. We propose a deep
multivariate mixture of Gaussians model for probabilistic object detection. The
covariances help to learn the relationship between the borders, and the mixture
components potentially learn different configurations of an occluded part.
Quantitatively, our model improves the AP of the baselines by 3.9% and 1.4% on
CrowdHuman and MS-COCO respectively with almost no computational or memory
overhead. Qualitatively, our model enjoys explainability since the resulting
covariance matrices and the mixture components help measure uncertainties.
