  Pedestrian detection in the crowd is a challenging task because of
intra-class occlusion. More prior information is needed for the detector to be
robust against it. Human head area is naturally a strong cue because of its
stable appearance, visibility and relative location to body. Inspired by it, we
adopt an extra branch to conduct semantic head detection in parallel with
traditional body branch. Instead of manually labeling the head regions, we use
weak annotations inferred directly from body boxes, which is named as `semantic
head'. In this way, the head detection is formulated into using a special part
of labeled box to detect the corresponding part of human body, which
surprisingly improves the performance and robustness to occlusion. Moreover,
the head-body alignment structure is explicitly explored by introducing
Alignment Loss, which functions in a self-supervised manner. Based on these, we
propose the head-body alignment net (HBAN) in this work, which aims to enhance
pedestrian detection by fully utilizing the human head prior. Comprehensive
evaluations are conducted to demonstrate the effectiveness of HBAN on
CityPersons dataset.
