Detecting Events in Crowds Through Changes in Geometrical Dimensions of
  Pedestrians
Matheus Schreiner Homrich da Silva Paulo Brossard de Souza Pinto Neto Rodolfo Migon Favaretto Soraia Raupp Musse
Security is an important topic in our contemporary world, and the ability to
automate the detection of any events of interest that can take place in a crowd
is of great interest to a population. We hypothesize that the detection of
events in videos is correlated with significant changes in pedestrian
behaviors. In this paper, we examine three different scenarios of crowd
behavior, containing both the cases where an event triggers a change in the
behavior of the crowd and two video sequences where the crowd and its motion
remain mostly unchanged. With both the videos and the tracking of the
individual pedestrians (performed in a pre-processed phase), we use Geomind, a
software we developed to extract significant data about the scene, in
particular, the geometrical features, personalities, and emotions of each
person. We then examine the output, seeking a significant change in the way
each person acts as a function of the time, that could be used as a basis to
identify events or to model realistic crowd actions. When applied to the games
area, our method can use the detected events to find some sort of pattern to be
then used in agent simulation. Results indicate that our hypothesis seems valid
in the sense that the visually observed events could be automatically detected
using GeoMind.