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We present a novel descriptor for crowd behavior analysis and anomalydetection. The goal is to measure by appropriate patterns the speed offormation and disintegration of groups in the crowd. This descriptor isinspired 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. Anappropriate measurement unit, named "trit" (trinary digit), represents threepossible dynamic states of groups on a certain frame. Our hypothesis is thatabrupt variations of the groups' number may be due to an anomalous event thatcan be accordingly detected, by translating these variations on temporaltrit-based sequence of strings which are significantly different from the onedescribing the "no-anomaly" one. Due to the peculiarity of the rationale behindthis work, relying on the number of groups, three different methods of peoplegroup's extraction are compared. Experiments are carried out on theMotion-Emotion benchmark data set. Reported results point out in which casesthe trit-based measurement of group dynamics allows us to detect the anomaly.Besides the promising performance of our approach, we show how it is correlatedwith the anomaly typology and the camera's perspective to the crowd's flow(frontal, lateral).

Detecting Anomalies from Video-Sequences: a Novel Descriptor