A Simple Approach Towards Efficient Partial-Copy Video Detection

Video copy detection is still an open problem as current approaches are not able to carry out the detection with enough efficacy and efficiency. These are desirable features in modern video-based applications requiring real-time processing in large scale video databases and without compromising detection performance, especially when facing non-simulated video attacks. These characteristics are also desirable in partial-copy detection, where the detection challenges increase when the video query contains short segments corresponding to a copied video, this is, partial-copies at frame level. Motivated by these issues, in this work we propose a video fingerprinting approach based on the extraction of a set of low-cost and independent binary global and local fingerprints. We tested our approach with a video dataset of real-copies and the results show that our method outperforms robust state-of-the-art methods in terms of detection scores and computational efficiency. The latter is achieved by processing only short segments of 1 second length, which takes a processing time of 44 ms.