Video fingerprinting based on moment invariants capturing appearance and motion

In this paper, we propose two video fingerprinting methods that are robust to both geometric and non-geometric modifications on content. Both of the proposed methods are based on computation of moment invariants as features from concentric circular regions. The two methods differ in the way they capture appearance and motion information from video. In one method, we capture motion information by computing a difference image between the current video frame and a temporal average video frame computed from a past window of video frames. This method captures appearance by computing moment invariants from concentric circular regions of a video frame. In the second method, we capture appearance and motion by projecting features onto two sets of basis functions and explicitly capture how the moment invariants change over the regions and over time. We present experimental results on both of these video fingerprinting comparing their performance in terms of robustness against attacks and sensitivity to content.