  This paper addresses the problem of head detection in crowded environments.
Our detection is based entirely on the geometric consistency across cameras
with overlapping fields of view, and no additional learning process is
required. We propose a fully unsupervised method for inferring scene and camera
geometry, in contrast to existing algorithms which require specific calibration
procedures. Moreover, we avoid relying on the presence of body parts other than
heads or on background subtraction, which have limited effectiveness under
heavy clutter. We cast the head detection problem as a stereo MRF-based
optimization of a dense pedestrian height map, and we introduce a constraint
which aligns the height gradient according to the vertical vanishing point
direction. We validate the method in an outdoor setting with varying pedestrian
density levels. With only three views, our approach is able to detect
simultaneously tens of heavily occluded pedestrians across a large, homogeneous
area.
