Temporal constraints in large optical flow estimation

Authors:
Agustín Salgado;Javier Sánchez
Affiliations:
Computer Science Department, University of Las Palmas de G.C, Las Palmas de G.C., Spain;Computer Science Department, University of Las Palmas de G.C, Las Palmas de G.C., Spain
Venue:
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Year:
2007

Citing 7
Cited 1

An Investigation of Smoothness Constraints for the Estimation of Displacement Vector Fields from Image Sequences

IEEE Transactions on Pattern Analysis and Machine Intelligence
Reliable Estimation of Dense Optical Flow Fields with Large Displacements

International Journal of Computer Vision
Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint

Journal of Mathematical Imaging and Vision
Extending the 'Oriented Smoothness Constraint' into the Temporal Domain and the Estimation of Derivatives of Optical Flow

ECCV '90 Proceedings of the First European Conference on Computer Vision
Optical-Flow Estimation while Preserving Its Discontinuities: A Variational Approach

ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
Symmetrical Dense Optical Flow Estimation with Occlusions Detection

International Journal of Computer Vision
Dense estimation and object-based segmentation of the optical flow with robust techniques

IEEE Transactions on Image Processing

Temporally consistent disparity and optical flow via efficient spatio-temporal filtering

PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I

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Abstract

The aim of this work is to propose a model for computing the optical flow in a sequence of images. We introduce a new temporal regularizer that is suitable for large displacements. We propose to decouple the spatial and temporal regularizations to avoid an incongruous formulation. For the spatial regularization we use the Nagel-Enkelmann operator and a newly designed temporal regularization. Our model is based on an energy functional that yields a partial differential equation (PDE). This PDE is embedded into a multipyramidal strategy to recover large displacements. A gradient descent technique is applied at each scale to reach the minimum.