Journal of Applied Optics, Volume. 46, Issue 2, 355(2025)

Fast optical flow estimation algorithm for edge GPU devices

Ke SHI1, Suzhen NIE1, Dongxing LI1、*, Jie CAO2, Yunlong SHENG1, Bin YAO1, and Honglin CHEN1
Author Affiliations
  • 1School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China
  • 2School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
  • show less
    References(25)

    [2] TEED Z, DENG J. DROID-SLAM: deep visual slam for monocular, stereo, and RGB-D cameras[J]. Advances in Neural Information Processing Systems, 34, 16558-16569(2021).

    [3] HU Y, HE Y, LI Y et al. Efficient semantic segmentation by altering resolutions for compressed videos[C], 22627-22637(2023).

    [4] DOSOVITSKIY A, FISCHER P, ILG E et al. FlowNet: learning optical flow with convolutional networks[C], 2758-2766(2015).

    [5] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C], 234-241(2015).

    [6] ILG E, MAYER N, SAIKIA T et al. FlowNet 2.0: evolution of optical flow estimation with deep networks[C], 2462-2470(2017).

    [7] RANJAN A, BLACK M J. Optical flow estimation using a spatial pyramid network[C], 4161-4170(2017).

    [8] SUN D, YANG X, LIU M Y et al. PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume[C], 8934-8943(2018).

    [9] TEED Z, DENG J. RAFT: recurrent all-pairs field transforms for optical flow[C], 402-419(2020).

    [10] VASWANI A, SHAZEER N, PARMAR N et al. Attention is all you need[J]. Advances in Neural Information Processing Systems, 30, 5998-6008(2017).

    [11] BROWN T, MANN B, RYDER N et al. Language models are few-shot learners[J]. Advances in Neural Information Processing Systems, 33, 1877-1901(2020).

    [12] JIANG S, CAMPBELL D, LU Y et al. Learning to estimate hidden motions with global motion aggregation[C], 9772-9781(2021).

    [13] XU H, ZHANG J, CAI J et al. GMFlow: learning optical flow via global matching[C], 8121-8130(2022).

    [14] HUANG Z, SHI X, ZHANG C et al. FlowFormer: a transformer architecture for optical flow[C], 668-685(2022).

    [15] KONG L, SHEN C, YANG J. FastFlowNet: a lightweight network for fast optical flow estimation[C], 10310-10316(2021).

    [16] MA N, ZHANG X, ZHENG H T et al. ShuffleNet V2: practical guidelines for efficient CNN architecture design[C], 116-131(2018).

    [19] MAYER N, ILG E, HAUSSER P et al. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation[C], 4040-4048(2016).

    [20] BUTLER D J, WULFF J, STANLEY G B et al. A naturalistic open source movie for optical flow evaluation[C], 611-625(2012).

    [21] MENZE M, GEIGER A. Object scene flow for autonomous vehicles[C], 3061-3070(2015).

    [22] KONDERMANN D, NAIR R, HONAUER K et al. The HCI benchmark suite: stereo and flow ground truth with uncertainties for urban autonomous driving[C], 19-28(2016).

    Tools

    Get Citation

    Copy Citation Text

    Ke SHI, Suzhen NIE, Dongxing LI, Jie CAO, Yunlong SHENG, Bin YAO, Honglin CHEN. Fast optical flow estimation algorithm for edge GPU devices[J]. Journal of Applied Optics, 2025, 46(2): 355

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Nov. 23, 2023

    Accepted: --

    Published Online: May. 13, 2025

    The Author Email: Dongxing LI (李东兴)

    DOI:10.5768/JAO202546.0202008

    Topics