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Scene Particles: Unregularized Particle Based Scene Flow Estimation

Hadfield, S and Bowden, R (2013) Scene Particles: Unregularized Particle Based Scene Flow Estimation IEEE Transactions on Pattern Analysis and Machine Intelligence, 36 (3), 3. pp. 564-576.

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In this paper, an algorithm is presented for estimating scene ?ow, which is a richer, 3D analogue of Optical Flow. The approach operates orders of magnitude faster than alternative techniques, and is well suited to further performance gains through parallelized implementation. The algorithm employs multiple hypothesis to deal with motion ambiguities, rather than the traditional smoothness constraints, removing oversmoothing errors and providing signi?cant performance improvements on benchmark data, over the previous state of the art. The approach is ?exible, and capable of operating with any combination of appearance and/or depth sensors, in any setup, simultaneously estimating the structure and motion if necessary. Additionally, the algorithm propagates information over time to resolve ambiguities, rather than performing an isolated estimation at each frame, as in contemporary approaches. Approaches to smoothing the motion ?eld without sacri?cing the bene?ts of multiple hypotheses are explored, and a probabilistic approach to Occlusion estimation is demonstrated, leading to 10% and 15% improved performance respectively. Finally, a data driven tracking approach is described, and used to estimate the 3D trajectories of hands during sign language, without the need to model complex appearance variations at each viewpoint.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Date : 26 August 2013
Identification Number : 10.1109/TPAMI.2013.162
Related URLs :
Additional Information : © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 27 Oct 2015 17:16
Last Modified : 27 Oct 2015 17:16

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