An Entropy Model for Identifying Loitering Behavior in Videos

Abstract

Loitering is a suspicious behavior that often leads to criminal actions, such as pick-pocketing and terrorist attacks. Tracking methods can determine suspicious behavior based on trajectory, but require continuous appearance and are difficult to scale up to multi-camera systems. Using the duration of appearance of features works on multiple cameras, but does not consider major aspects of loitering behavior, such as repeated appearance and trajectory of candidates. We introduce an entropy model that maps the location of a person’s features on a heatmap. It can be used as a substitution for trajectory tracking across multiple surveillance cameras. We evaluate our method over several datasets and compare it to other loitering detection methods. The results show that our approach has similar results to state of the art, but can provide additional interesting candidates.