  Heavy occlusion and dense gathering in crowd scene make pedestrian detection
become a challenging problem, because it's difficult to guess a precise full
bounding box according to the invisible human part. To crack this nut, we
propose a mechanism called Visible Feature Guidance (VFG) for both training and
inference. During training, we adopt visible feature to regress the
simultaneous outputs of visible bounding box and full bounding box. Then we
perform NMS only on visible bounding boxes to achieve the best fitting full box
in inference. This manner can alleviate the incapable influence brought by NMS
in crowd scene and make full bounding box more precisely. Furthermore, in order
to ease feature association in the post application process, such as pedestrian
tracking, we apply Hungarian algorithm to associate parts for a human instance.
Our proposed method can stably bring about 2~3% improvements in mAP and AP50
for both two-stage and one-stage detector. It's also more effective for MR-2
especially with the stricter IoU. Experiments on Crowdhuman, Cityperson,
Caltech and KITTI datasets show that visible feature guidance can help detector
achieve promisingly better performances. Moreover, parts association produces a
strong benchmark on Crowdhuman for the vision community.
