Dense Optical Flow Estimation Using Sparse Regularizers from Reduced Measurements

• Published in: IEEE access 2024-01, Vol.12, p.1-1
• Main Authors: Nawaz, Muhammad Wasim, Bouzerdoum, Abdesselam, Rahman, Muhammad Mahboob Ur, Abbas, Ghulam, Rashid, Faizan
• Format: Article
• Language: English
• Subjects: Activity recognition; Anisotropic; Computer vision; Critical components; Energy management; Energy Minimization; Estimation; Image motion analysis; Mathematical analysis; Minimization; Motion control; Motion Discontinuities; Motion simulation; Object motion; Object recognition; Optical flow; Optical flow (image analysis); Optical tracking; Regularization; Regularization methods; Robustness (mathematics); Sparse Regularizers; Sparsity; Total Variation
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3382818

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 ℓ1 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.