  Occlusion is very challenging in pedestrian detection. In this paper, we
propose a simple yet effective method named V2F-Net, which explicitly
decomposes occluded pedestrian detection into visible region detection and full
body estimation. V2F-Net consists of two sub-networks: Visible region Detection
Network (VDN) and Full body Estimation Network (FEN). VDN tries to localize
visible regions and FEN estimates full-body box on the basis of the visible
box. Moreover, to further improve the estimation of full body, we propose a
novel Embedding-based Part-aware Module (EPM). By supervising the visibility
for each part, the network is encouraged to extract features with essential
part information. We experimentally show the effectiveness of V2F-Net by
conducting several experiments on two challenging datasets. V2F-Net achieves
5.85% AP gains on CrowdHuman and 2.24% MR-2 improvements on CityPersons
compared to FPN baseline. Besides, the consistent gain on both one-stage and
two-stage detector validates the generalizability of our method.
