Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
University of California, Berkeley
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Abstract
Optical flow estimation is classically marked by the requirement of dense sampling in time. While coarse-to-fine warping schemes have somehow relaxed this constraint, there is an inherent dependency between the scale of structures and the velocity that can be estimated. This particularly renders the estimation of detailed human motion problematic, as small body parts can move very fast. In this paper, we present a way to approach this problem by integrating rich descriptors into the variational optical flow setting. This way we can estimate a dense optical flow field with almost the same high accuracy as known from variational optical flow, while reaching out to new domains of motion analysis where the…
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Authors
2Topics & keywords
Topics
Keywords
- Optical flow
- Image warping
- Computer science
- Motion estimation
- Flow (mathematics)
- Constraint (computer-aided design)
- Sampling (signal processing)
- Matching (statistics)
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