Robust Optical Flow Computation: A Higher-Order Differential Approach

• URL: http://arxiv.org/abs/2410.09563v1
• Date: Sat, 12 Oct 2024 15:20:11 GMT
• Title: Robust Optical Flow Computation: A Higher-Order Differential Approach
• Authors: Chanuka Algama, Kasun Amarasinghe,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the domain of computer vision, optical flow stands as a cornerstone for unraveling dynamic visual scenes. However, the challenge of accurately estimating optical flow under conditions of large nonlinear motion patterns remains an open question. The image flow constraint is vulnerable to substantial displacements, and rapid spatial transformations. Inaccurate approximations inherent in numerical differentiation techniques can further amplify such intricacies. In response, this research proposes an innovative algorithm for optical flow computation, utilizing the higher precision of second-order Taylor series approximation within the differential estimation framework. By embracing this mathematical underpinning, the research seeks to extract more information about the behavior of the function under complex real-world scenarios and estimate the motion of areas with a lack of texture. An impressive showcase of the algorithm's capabilities emerges through its performance on renowned optical flow benchmarks such as KITTI (2015) and Middlebury. The average endpoint error (AEE), which computes the Euclidian distance between the calculated flow field and the ground truth flow field, stands notably diminished, validating the effectiveness of the algorithm in handling complex motion patterns.

Related papers

• Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation [34.529280562470746]
  We introduce a novel self-supervised loss combining the Contrast Maximization framework with a non-linear motion prior in the form of pixel-level trajectories. Their effectiveness is demonstrated in two scenarios: In dense continuous-time motion estimation, our method improves the zero-shot performance of a synthetically trained model by 29%.
  arXiv  Detail & Related papers  (2024-07-15T15:18:28Z)
• Rethink Predicting the Optical Flow with the Kinetics Perspective [1.7901503554839604]
  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.
  arXiv  Detail & Related papers  (2024-05-21T05:47:42Z)
• Motion-Aware Video Frame Interpolation [49.49668436390514]
  We introduce a Motion-Aware Video Frame Interpolation (MA-VFI) network, which directly estimates intermediate optical flow from consecutive frames. It not only extracts global semantic relationships and spatial details from input frames with different receptive fields, but also effectively reduces the required computational cost and complexity.
  arXiv  Detail & Related papers  (2024-02-05T11:00:14Z)
• GAFlow: Incorporating Gaussian Attention into Optical Flow [62.646389181507764]
  We push Gaussian Attention (GA) into the optical flow models to accentuate local properties during representation learning. We introduce a novel Gaussian-Constrained Layer (GCL) which can be easily plugged into existing Transformer blocks. For reliable motion analysis, we provide a new Gaussian-Guided Attention Module (GGAM)
  arXiv  Detail & Related papers  (2023-09-28T07:46:01Z)
• SSTM: Spatiotemporal Recurrent Transformers for Multi-frame Optical Flow Estimation [0.0]
  In optical flow estimates in and near cluded regions, and out-of-boundary regions are two of the current significant limitations of optical flow estimation algorithms. Recent state-of-the-art optical flow estimation algorithms are two-frame based methods where optical flow is estimated sequentially for each consecutive image pair in a sequence. We propose a learning-based multi-frame optical flow estimation method that estimates two or more consecutive optical flows in parallel from multi-frame image sequences.
  arXiv  Detail & Related papers  (2023-04-26T23:39:40Z)
• Sensor-Guided Optical Flow [53.295332513139925]
  This paper proposes a framework to guide an optical flow network with external cues to achieve superior accuracy on known or unseen domains. We show how these can be obtained by combining depth measurements from active sensors with geometry and hand-crafted optical flow algorithms.
  arXiv  Detail & Related papers  (2021-09-30T17:59:57Z)
• 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)
• STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation [64.99259320624148]
  We present a new lightweight CNN-based algorithm for multi-frame optical flow estimation. The resulting STaRFlow algorithm gives state-of-the-art performances on MPI Sintel and Kitti2015.
  arXiv  Detail & Related papers  (2020-07-10T17:01:34Z)
• 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.