  We present a multi-camera 3D pedestrian detection method that does not need
to train using data from the target scene. We estimate pedestrian location on
the ground plane using a novel heuristic based on human body poses and person's
bounding boxes from an off-the-shelf monocular detector. We then project these
locations onto the world ground plane and fuse them with a new formulation of a
clique cover problem. We also propose an optional step for exploiting
pedestrian appearance during fusion by using a domain-generalizable person
re-identification model. We evaluated the proposed approach on the challenging
WILDTRACK dataset. It obtained a MODA of 0.569 and an F-score of 0.78, superior
to state-of-the-art generalizable detection techniques.
