Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements

doi:10.1109/ACCESS.2024.3382818

Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements

2024-01-01

1

OA

AI

M

Muhammad Wasim Nawaz

A

Abdesselam Bouzerdoum

M

Muhammad Mahboob Ur Rahman

DOI：10.1109/ACCESS.2024.3382818

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Optical flow is the pattern of apparent motion of objects in a scene. The computation of optical flow is a critical component in numerous computer vision tasks such as object detection, visual object tracking, and activity recognition. Despite a lot of research, efficiently managing abrupt changes in motion remains a challenge in motion estimation. This paper proposes novel variational regularization methods to address this problem since they allow combining different mathematical concepts into a joint energy minimization framework. In this work, we incorporate concepts from signal sparsity into variational regularization for motion estimation. The proposed regularization uses robust l(1) norm, which promotes sparsity and handles motion discontinuities. By using this regularization, we promote the sparsity of the optical flow gradient. This sparsity helps recover a signal even with just a few measurements. We explore recovering optical flow from a limited set of linear measurements using this regularizer. Our findings show that leveraging the sparsity of the derivatives of optical flow reduces computational complexity and memory needs.

Keywords:

Energy minimization

motion discontinuities

optical flow

sparse regularizers

total variation

Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements

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