  We present a novel descriptor for crowd behavior analysis and anomaly
detection. The goal is to measure by appropriate patterns the speed of
formation and disintegration of groups in the crowd. This descriptor is
inspired by the concept of one-dimensional local binary patterns: in our case,
such patterns depend on the number of group observed in a time window. An
appropriate measurement unit, named "trit" (trinary digit), represents three
possible dynamic states of groups on a certain frame. Our hypothesis is that
abrupt variations of the groups' number may be due to an anomalous event that
can be accordingly detected, by translating these variations on temporal
trit-based sequence of strings which are significantly different from the one
describing the "no-anomaly" one. Due to the peculiarity of the rationale behind
this work, relying on the number of groups, three different methods of people
group's extraction are compared. Experiments are carried out on the
Motion-Emotion benchmark data set. Reported results point out in which cases
the trit-based measurement of group dynamics allows us to detect the anomaly.
Besides the promising performance of our approach, we show how it is correlated
with the anomaly typology and the camera's perspective to the crowd's flow
(frontal, lateral).
