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2017 | OriginalPaper | Chapter

Motion Estimation from Image Sequences: A Fractional Order Total Variation Model

Authors : Pushpendra Kumar, Balasubramanian Raman

Published in: Proceedings of International Conference on Computer Vision and Image Processing

Publisher: Springer Singapore

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Abstract

In this paper, a fractional order total variation model is introduced in the estimation of motion field. In particular, the proposed model generalizes the integer order total variation models. The motion estimation is carried out in terms optical flow. The presented model is made using a quadratic and total variation terms. This mathematical formulation makes the model robust against outliers and preserves discontinuities. However, it is difficult to solve the presented model due to the non-differentiability nature of total variation term. For this purpose, the Grünwald-Letnikov derivative is used as a discretization scheme to discretize the fractional order derivative. The resulting formulation is solved by using a more efficient algorithm. Experimental results on various datasets verify the validity of the proposed model.

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Metadata
Title
Motion Estimation from Image Sequences: A Fractional Order Total Variation Model
Authors
Pushpendra Kumar
Balasubramanian Raman
Copyright Year
2017
Publisher
Springer Singapore
Book
Proceedings of International Conference on Computer Vision and Image Processing

Print ISBN: 978-981-10-2106-0

Electronic ISBN: 978-981-10-2107-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-2107-7_27