Robust Video Synchronization using Unsupervised Deep Learning.
Year: 2016
Type: preprint
Abstract: Aligning video sequences is a fundamental yet still unsolved component for awide range of applications in computer graphics and vision. Especially when
targeting video clips containing an extensively varying appearance. Using
recent advances in deep learning, we present a scalable and robust method for
computing optimal non-linear temporal video alignments. The presented algorithm
learns to retrieve and match similar video frames from input sequences without
any human interaction or additional annotations in an unsupervised fashion. An
iterative scheme is presented which leverages on the nature of the videos
themselves in order to remove the need for labels. We incorporate a variation
of Dijkstra's shortest-path algorithm for extracting meaningful training
examples as well as a robust video alignment. While previous methods assume
similar settings as weather conditions, season and illumination, our approach
is able to robustly align videos regardless of such noise. This provides new
ways of compositing non-seasonal video clips from data recorded months apart.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2534656029