Tamper detection for active surveillance systems

Abstract

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 address this important problem for an active camera. We detect 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 system on these data and the results are encouraging with a very high detecting rate and relatively few false positives.