Tamper Detection for Active Surveillance Systems

If surveillance data are corrupted they are of no use to
neither manually post-investigation nor automatic video
analysis. It is therefore critical to automatically be able
to detect tampering events such as defocusing, occlusion
and displacement. In this work we for the first time ad-
dress this important problem for an active camera. We de-
tect these events by first comparing the incoming frames to
a background model using features relevant for the three
different tampering types. Individual detectors are then
developed capable of monitoring long video sequences and
indicating the occurrence of different tampering events.
In order to assess the developed methods we have collected
a large data set, which contains sequences from different
active cameras at different scenarios. We evaluate our sys-
tem on these data and the results are encouraging with a
very high detecting rate and relatively few false positives.