  Human body orientation estimation (HBOE) is widely applied into various
applications, including robotics, surveillance, pedestrian analysis and
autonomous driving. Although many approaches have been addressing the HBOE
problem from specific under-controlled scenes to challenging in-the-wild
environments, they assume human instances are already detected and take a well
cropped sub-image as the input. This setting is less efficient and prone to
errors in real application, such as crowds of people. In the paper, we propose
a single-stage end-to-end trainable framework for tackling the HBOE problem
with multi-persons. By integrating the prediction of bounding boxes and
direction angles in one embedding, our method can jointly estimate the location
and orientation of all bodies in one image directly. Our key idea is to
integrate the HBOE task into the multi-scale anchor channel predictions of
persons for concurrently benefiting from engaged intermediate features.
Therefore, our approach can naturally adapt to difficult instances involving
low resolution and occlusion as in object detection. We validated the
efficiency and effectiveness of our method in the recently presented benchmark
MEBOW with extensive experiments. Besides, we completed ambiguous instances
ignored by the MEBOW dataset, and provided corresponding weak body-orientation
labels to keep the integrity and consistency of it for supporting studies
toward multi-persons. Our work is available at
https://github.com/hnuzhy/JointBDOE.
