  Pedestrian detection in a crowd is a challenging task due to a high number of
mutually-occluding human instances, which brings ambiguity and optimization
difficulties to the current IoU-based ground truth assignment procedure in
classical object detection methods. In this paper, we develop a unique
perspective of pedestrian detection as a variational inference problem. We
formulate a novel and efficient algorithm for pedestrian detection by modeling
the dense proposals as a latent variable while proposing a customized Auto
Encoding Variational Bayes (AEVB) algorithm. Through the optimization of our
proposed algorithm, a classical detector can be fashioned into a variational
pedestrian detector. Experiments conducted on CrowdHuman and CityPersons
datasets show that the proposed algorithm serves as an efficient solution to
handle the dense pedestrian detection problem for the case of single-stage
detectors. Our method can also be flexibly applied to two-stage detectors,
achieving notable performance enhancement.
