Dense Optical Flow Estimation Using Sparse Regularizers from Reduced Measurements

2024-01-12 · Muhammad Wasim Nawaz, Abdesselam Bouzerdoum, Muhammad Mahboob Ur Rahman, GHULAM ABBAS, Faizan Rashid

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 a 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.

📄 PDF Abstract BibTeX arXiv:2401.06396

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Tasks

Activity RecognitionMotion Estimationobject-detection Object Detection Object Tracking Optical Flow Estimation Visual Object Tracking

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