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Efficient Subframe Video Alignment Using Short Descriptors

Georgios Evangelidis 1 Christian Bauckhage 2
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
2 Multimedia Pattern Recognition
Fraunhofer IAIS - Fraunhofer Institute for Intelligent Analysis and Information Systems
Abstract : This paper addresses the problem of video alignment. We present efficient approaches that allow for spatiotemporal alignment of two sequences. Unlike most related works, we consider independently moving cameras that capture a 3D scene at different times. The novelty of the proposed method lies in the adaptation and extension of an efficient information retrieval framework that casts the sequences as an image database and a set of query frames, respectively. The efficient retrieval builds on the recently proposed quad descriptor. In this context, we define the 3D Vote Space (VS) by aggregating votes through a multiquerying (multiscale) scheme and we present two solutions based on VS entries; a causal solution that permits online synchronization and a global solution through multiscale dynamic programming. In addition, we extend the recently introduced ECC image-alignment algorithm to the temporal dimension that allows for spatial registration and synchronization refinement with subframe accuracy. We investigate full search and quantization methods for short descriptors and we compare the proposed schemes with the state of the art. Experiments with real videos by moving or static cameras demonstrate the efficiency of the proposed method and verify its effectiveness with respect to spatiotemporal alignment accuracy.
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Submitted on : Tuesday, September 24, 2013 - 3:10:47 PM
Last modification on : Tuesday, November 24, 2020 - 4:36:03 PM
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Georgios Evangelidis, Christian Bauckhage. Efficient Subframe Video Alignment Using Short Descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2013, 35 (10), pp.2371-2386. ⟨10.1109/TPAMI.2013.56⟩. ⟨hal-00862002v2⟩



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