2 research outputs found
Dense Optical Flow Estimation Using Sparse Regularizers from Reduced Measurements
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.Comment: 12 pages, 9 figures, and 3 table
Optical flow refinement using iterative propagation under colour, proximity and flow reliability constraints
International audienceThis study proposes a strategy to refine optical flow based on the estimated reliability maps. These maps are firstly estimated a posteriori after the motion estimation by the well-known Kanade–Lucas–Tomasi (KLT). With two new defined criteria based, respectively, on the optical flow local variance and the temporal evolution of the KLT residuals, a global refinement of the motion map is then carried out through two stages under the control of the reliability measures and the colour local homogeneousness. According to the experiments performed on the Middlebury dataset, the authors' reliability measures prove to be a good indicator for the quality of the estimation. Indeed, the correction process increases the global reliability measures and reduces the global errors in a significant way. The experiments show that the quality is higher than classical estimation methods and ranked at 88/168 on Middlebury websit