Video Temporal Alignment for Object Viewpoint

Anestis Papazoglou, Luca Del Pero, Vittorio Ferrari

Abstract: We address the problem of temporally aligning semantically similar videos, for example two videos of cars on different tracks. We present an alignment method that establishes frame-to-frame correspondences such that the two cars are seen from a similar viewpoint (e.g. facing right), while also being temporally smooth and visually pleasing. Unlike previous works, we do not assume that the videos show the same scripted sequence of events. We compare against three alternative methods, including the popular DTW algorithm, on a new dataset of realistic videos collected from the internet. We perform a comprehensive evaluation using a novel protocol that includes both quantitative measures and a user study on visual pleasingness.