  Video surveillance always had a negative connotation, among others because of
the loss of privacy and because it may not automatically increase public
safety. If it was able to detect atypical (i.e. dangerous) situations in real
time, autonomously and anonymously, this could change. A prerequisite for this
is a reliable automatic detection of possibly dangerous situations from video
data. This is done classically by object extraction and tracking. From the
derived trajectories, we then want to determine dangerous situations by
detecting atypical trajectories. However, due to ethical considerations it is
better to develop such a system on data without people being threatened or even
harmed, plus with having them know that there is such a tracking system
installed. Another important point is that these situations do not occur very
often in real, public CCTV areas and may be captured properly even less. In the
artistic project leave a trace the tracked objects, people in an atrium of a
institutional building, become actor and thus part of the installation.
Visualisation in real-time allows interaction by these actors, which in turn
creates many atypical interaction situations on which we can develop our
situation detection. The data set has evolved over three years and hence, is
huge. In this article we describe the tracking system and several approaches
for the detection of atypical trajectories.
