Near-duplicate detection and alignment for multi-view videos

The increasing popularity of video sharing platforms (e.g., YouTube, Vimeo, etc.) has determined the widespread diffusion of near-duplicate videos, i.e., sequences obtained applying different editing operations to the same original clip. However, it is also possible to come across sequences referring to the same specific event shot from different viewpoints. This is a very common situation that arises when analyzing user-generated content acquired with mobile devices. Therefore, for some applications, it can be useful to extend the concept of near-duplicates considering also all the videos (and their edited versions) referring to the same event even if shot from different viewpoints. In this paper we consider such challenging scenario. More specifically, we focus on the problem of multi-view near-duplicate video detection and temporal alignment. In doing so, we show the limitations of a state-of-the-art algorithm based on robust hashing, and propose a processing pipeline that allows to deal also with sequences taken from significantly different viewpoints.