  Pedestrians are arguably one of the most safety-critical road users to
consider for autonomous vehicles in urban areas. In this paper, we address the
problem of jointly detecting pedestrians and recognizing 32 pedestrian
attributes from a single image. These encompass visual appearance and behavior,
and also include the forecasting of road crossing, which is a main safety
concern. For this, we introduce a Multi-Task Learning (MTL) model relying on a
composite field framework, which achieves both goals in an efficient way. Each
field spatially locates pedestrian instances and aggregates attribute
predictions over them. This formulation naturally leverages spatial context,
making it well suited to low resolution scenarios such as autonomous driving.
By increasing the number of attributes jointly learned, we highlight an issue
related to the scales of gradients, which arises in MTL with numerous tasks. We
solve it by normalizing the gradients coming from different objective functions
when they join at the fork in the network architecture during the backward
pass, referred to as fork-normalization. Experimental validation is performed
on JAAD, a dataset providing numerous attributes for pedestrian analysis from
autonomous vehicles, and shows competitive detection and attribute recognition
results, as well as a more stable MTL training.
