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Motion analysis and tracking often relies on multi-modal signals, e.g., video, depth map, motion capture (MoCap), due to the completeness of information they jointly provide. The joint analysis of multimodal signals requires to know the correct timing, i.e., the signals to be aligned. In this paper we propose an approach to automatically estimate the correct matching and alignment between a video and a MoCap recording acquired from the same session, based on the multi-dimensional correlation of velocity-based features extracted from the two recordings. We validate our approach over a dataset of dance recordings of four genres, and we achieve promising results for both the alignment and matching scenarios.

This paper introduces an accurate approach for synchronization (temporal alignment) between two video sequences of the same dynamic scene captured by uncalibrated cameras. With the homography assumption in spatial domain, an iterative procedure that successively achieves the alignment in space and time is proposed and its convergence is experimentally verified. Subframe accuracy is achieved by extending the existing image subpixel registration scheme to subframe video synchronization. In order to demonstrate the accuracy of the proposed method, we adopt a novel use of audio signals for their high sampling rate to obtain the synchronization ground-truth. The proposed video synchronization technique has potential use in temporal super-resolution, image-based rendering and tele-immersion.

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