Temporal synchronization of non-overlapping videos using known object motion
Pattern Recognition Letters
Aligning spatio-temporal signals on a special manifold
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Video synchronization as one-class learning
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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In this paper, we consider the problem of estimating the spatiotemporal alignment between N unsynchronized video sequences of the same dynamic 3D scene, captured from distinct viewpoints. Unlike most existing methods, which work for N=2 and rely on a computationally intensive search in the space of temporal alignments, we present a novel approach that reduces the problem for general N to the robust estimation of a single line in {{\hbox{\rlap{I}\kern 2.0pt{\hbox{R}}}}}^{N}. This line captures all temporal relations between the sequences and can be computed without any prior knowledge of these relations. Considering that the spatial alignment is captured by the parameters of fundamental matrices, an iterative algorithm is used to refine simultaneously the parameters representing the temporal and spatial relations between the sequences. Experimental results with real-world and synthetic sequences show that our method can accurately align the videos even when they have large misalignments (e.g., hundreds of frames), when the problem is seemingly ambiguous (e.g., scenes with roughly periodic motion), and when accurate manual alignment is difficult (e.g., due to slow-moving objects).