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Pedestrian detection benefits greatly from deep convolutional neural networks(CNNs). However, it is inherently hard for CNNs to handle situations in thepresence of occlusion and scale variation. In this paper, we propose W$^3$Net,which attempts to address above challenges by decomposing the pedestriandetection task into \textbf{\textit{W}}here, \textbf{\textit{W}}hat and\textbf{\textit{W}}hether problem directing against pedestrian localization,scale prediction and classification correspondingly. Specifically, for apedestrian instance, we formulate its feature by three steps. i) We generate abird view map, which is naturally free from occlusion issues, and scan allpoints on it to look for suitable locations for each pedestrian instance. ii)Instead of utilizing pre-fixed anchors, we model the interdependency betweendepth and scale aiming at generating depth-guided scales at different locationsfor better matching instances of different sizes. iii) We learn a latent vectorshared by both visual and corpus space, by which false positives with similarvertical structure but lacking human partial features would be filtered out. Weachieve state-of-the-art results on widely used datasets (Citypersons andCaltech). In particular. when evaluating on heavy occlusion subset, our resultsreduce MR$^{-2}$ from 49.3$\%$ to 18.7$\%$ on Citypersons, and from 45.18$\%$to 28.33$\%$ on Caltech.

Where, What, Whether: Multi-modal Learning Meets Pedestrian Detection