Rethink Predicting the Optical Flow with the Kinetics Perspective

• URL: http://arxiv.org/abs/2405.12512v1
• Date: Tue, 21 May 2024 05:47:42 GMT
• Title: Rethink Predicting the Optical Flow with the Kinetics Perspective
• Authors: Yuhao Cheng, Siru Zhang, Yiqiang Yan,
• Abstract summary: Optical flow estimation is one of the fundamental tasks in low-level computer vision. From the apparent aspect, the optical flow can be viewed as the correlation between the pixels in consecutive frames. We propose a method combining the apparent and kinetics information from this motivation.
• Score: 1.7901503554839604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical flow estimation is one of the fundamental tasks in low-level computer vision, which describes the pixel-wise displacement and can be used in many other tasks. From the apparent aspect, the optical flow can be viewed as the correlation between the pixels in consecutive frames, so continuously refining the correlation volume can achieve an outstanding performance. However, it will make the method have a catastrophic computational complexity. Not only that, the error caused by the occlusion regions of the successive frames will be amplified through the inaccurate warp operation. These challenges can not be solved only from the apparent view, so this paper rethinks the optical flow estimation from the kinetics viewpoint.We propose a method combining the apparent and kinetics information from this motivation. The proposed method directly predicts the optical flow from the feature extracted from images instead of building the correlation volume, which will improve the efficiency of the whole network. Meanwhile, the proposed method involves a new differentiable warp operation that simultaneously considers the warping and occlusion. Moreover, the proposed method blends the kinetics feature with the apparent feature through the novel self-supervised loss function. Furthermore, comprehensive experiments and ablation studies prove that the proposed novel insight into how to predict the optical flow can achieve the better performance of the state-of-the-art methods, and in some metrics, the proposed method outperforms the correlation-based method, especially in situations containing occlusion and fast moving. The code will be public.

Related papers

• Robust Optical Flow Computation: A Higher-Order Differential Approach [0.0]
  This research proposes an innovative algorithm for optical flow computation, utilizing the higher precision of second-order Taylor series approximation. An impressive showcase of the algorithm's capabilities emerges through its performance on optical flow benchmarks such as KITTI and Middlebury.
  arXiv  Detail & Related papers  (2024-10-12T15:20:11Z)
• Neural refractive index field: Unlocking the Potential of Background-oriented Schlieren Tomography in Volumetric Flow Visualization [6.748519362625069]
  This work presents an innovative reconstruction approach termed neural refractive index field (NeRIF) NeRIF implicitly represents the flow field with a neural network, which is trained with tailored strategies. Both numerical simulations and experimental demonstrations on turbulent Bunsen flames suggest that our approach can significantly improve the reconstruction accuracy and spatial resolution.
  arXiv  Detail & Related papers  (2024-09-23T05:40:50Z)
• Efficient Diffusion Model for Image Restoration by Residual Shifting [63.02725947015132]
  This study proposes a novel and efficient diffusion model for image restoration. Our method avoids the need for post-acceleration during inference, thereby avoiding the associated performance deterioration. Our method achieves superior or comparable performance to current state-of-the-art methods on three classical IR tasks.
  arXiv  Detail & Related papers  (2024-03-12T05:06:07Z)
• An Optimization-based Deep Equilibrium Model for Hyperspectral Image Deconvolution with Convergence Guarantees [71.57324258813675]
  We propose a novel methodology for addressing the hyperspectral image deconvolution problem. A new optimization problem is formulated, leveraging a learnable regularizer in the form of a neural network. The derived iterative solver is then expressed as a fixed-point calculation problem within the Deep Equilibrium framework.
  arXiv  Detail & Related papers  (2023-06-10T08:25:16Z)
• Dense Optical Flow from Event Cameras [55.79329250951028]
  We propose to incorporate feature correlation and sequential processing into dense optical flow estimation from event cameras. Our proposed approach computes dense optical flow and reduces the end-point error by 23% on MVSEC.
  arXiv  Detail & Related papers  (2021-08-24T07:39:08Z)
• PCA Event-Based Otical Flow for Visual Odometry [0.0]
  We present a Principal Component Analysis approach to the problem of event-based optical flow estimation. We show that the best variant of our proposed method, dedicated to the real-time context of visual odometry, is about two times faster compared to state-of-the-art implementations.
  arXiv  Detail & Related papers  (2021-05-08T18:30:44Z)
• Unsupervised Motion Representation Enhanced Network for Action Recognition [4.42249337449125]
  Motion representation between consecutive frames has proven to have great promotion to video understanding. TV-L1 method, an effective optical flow solver, is time-consuming and expensive in storage for caching the extracted optical flow. We propose UF-TSN, a novel end-to-end action recognition approach enhanced with an embedded lightweight unsupervised optical flow estimator.
  arXiv  Detail & Related papers  (2021-03-05T04:14:32Z)
• Optical Flow Estimation from a Single Motion-blurred Image [66.2061278123057]
  Motion blur in an image may have practical interests in fundamental computer vision problems. We propose a novel framework to estimate optical flow from a single motion-blurred image in an end-to-end manner.
  arXiv  Detail & Related papers  (2021-03-04T12:45:18Z)
• What Matters in Unsupervised Optical Flow [51.45112526506455]
  We compare and analyze a set of key components in unsupervised optical flow. We construct a number of novel improvements to unsupervised flow models. We present a new unsupervised flow technique that significantly outperforms the previous state-of-the-art.
  arXiv  Detail & Related papers  (2020-06-08T19:36:26Z)
• Joint Unsupervised Learning of Optical Flow and Egomotion with Bi-Level Optimization [59.9673626329892]
  We exploit the global relationship between optical flow and camera motion using epipolar geometry. We use implicit differentiation to enable back-propagation through the lower-level geometric optimization layer independent of its implementation.
  arXiv  Detail & Related papers  (2020-02-26T22:28:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.