A Spatial-Temporal-Scale Registration Approach for Video Copy Detection

Video copy detection is an active research field in copyright control, business intelligence and advertisement monitor etc. The main issues are transformation-invariant feature extraction and robust registration in object level. This paper proposes a novel video copy detection approach based on spatial-temporal-scale registration. In detail, we first build interesting points' trajectories by speeded up robust features (SURF). Then we use an efficient voting based spatial-temporal-scale registration approach to estimate the optimal transformation parameters and achieve the final video copy detection results by propagations of video segments in both spatial-temporal and scale directions. To speed up the detection speed, we use local sensitive hash indexing (LSH) to index trajectories for fast queries of candidate trajectories. Compared with existing approaches, our approach can detect many kinds of copy transformations including cropping, zoom in/out, camcording and re-encoding etc. Extensive experiments on 200 hours of videos demonstrate the effectiveness of our approach.