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The paper presents an approach for establishing correspondences in time and in space between two different video sequences of the same dynamic scene, recorded by stationary uncalibrated video cameras. The method simultaneously estimates both spatial alignment as well as temporal synchronization (temporal alignment) between the two sequences, using all available spatio-temporal information. Temporal variations between image frames (such as moving objects or changes in scene illumination) are powerful cues for alignment, which cannot be exploited by standard image-to-image alignment techniques. We show that by folding spatial and temporal cues into a single alignment framework, situations which are inherently ambiguous for traditional image-to-image alignment methods, are often uniquely resolved by sequence-to-sequence alignment. We also present a "direct" method for sequence-to-sequence alignment. The algorithm simultaneously estimates spatial and temporal alignment parameters directly from measurable sequence quantities, without requiring prior estimation of point correspondences, frame correspondences, or moving object detection. Results are shown on real image sequences taken by multiple video cameras.
Caspi et al. (Thu,) studied this question.