  We investigate in real-life conditions and with very high accuracy the
dynamics of body rotation, or yawing, of walking pedestrians - an highly
complex task due to the wide variety in shapes, postures and walking gestures.
We propose a novel measurement method based on a deep neural architecture that
we train on the basis of generic physical properties of the motion of
pedestrians. Specifically, we leverage on the strong statistical correlation
between individual velocity and body orientation: the velocity direction is
typically orthogonal with respect to the shoulder line. We make the reasonable
assumption that this approximation, although instantaneously slightly
imperfect, is correct on average. This enables us to use velocity data as
training labels for a highly-accurate point-estimator of individual
orientation, that we can train with no dedicated annotation labor. We discuss
the measurement accuracy and show the error scaling, both on synthetic and
real-life data: we show that our method is capable of estimating orientation
with an error as low as 7.5 degrees. This tool opens up new possibilities in
the studies of human crowd dynamics where orientation is key. By analyzing the
dynamics of body rotation in real-life conditions, we show that the
instantaneous velocity direction can be described by the combination of
orientation and a random delay, where randomness is provided by an
Ornstein-Uhlenbeck process centered on an average delay of 100ms. Quantifying
these dynamics could have only been possible thanks to a tool as precise as
that proposed.
