[1] Horn B K P,Schunck B G.Determining optical flow[J].Artificial Intelligence,1980,17(1-3):185-203.
[2] 张聪炫,陈震,黎明.单目图像序列光流三维重建技术研究综述[J].电子学报,2016,44(12):3044-3052. ZHANG Cong-xuan,CHEN Zhen,LI Ming.Review of the 3D reconstruction technology based on optical flow of monocular image sequence[J].Acta Electronica Sinica,2016,44(12):3044-3052.(in Chinese).
[3] 陈震,张聪炫,晏文敬,吴燕平.基于图像局部结构的区域匹配变分光流算法[J].电子学报,2015,43(11):2200-2209. CHEN Zhen,ZHANG Cong-xuan,YAN Wen-jing,WU Yan-ping.Region matching variational optical flow algorithm based on image local structure[J].Acta Electronica Sinica,2015,43(11):2200-2209.(in Chinese).
[4] Bailer C,Taetz B,Stricker D.Flow fields:Dense correspondence fields for highly accurate large displacement optical flow estimation[A].International Conference on Computer Vision[C].Boston:IEEE,2015.4015-4023.
[5] Heeger D J.Optical flow using spatiotemporal filters[J].International Journal of Computer Vision,1988,1(4):279-302.
[6] Fleet D J,Jepson A D.Computation of component image velocity from local phase information[J].InternationalJournal of Computer Vision,1990,5(1):77-104.
[7] Zhang C X,Chen Z,Wang M R,Li M,Jiang S F.Robust non-local TV-L1 optical flow estimation with occlusion detection[J].IEEE Transactions on Image Processing,2017,26(8):4055-4067.
[8] 葛利跃,张聪炫,陈震,黎明,陈昊.相互结构引导滤波TV-L1变分光流估计[J].电子学报,2019,47(3):707-713. GE Li-yue,ZHANG Cong-xuan,CHEN Zhen,LI Ming,CHEN Hao.Mutual-structure guided filtering based TV-L1 optical flow estimation[J].Acta Electronica Sinica,2015,43(11):2200-2209.(in Chinese).
[9] 张聪炫,陈震,熊帆,黎明,葛利跃,陈昊.非刚性稠密匹配大位移运动光流估计[J].电子学报,2019,47(6):1316-1323. ZHANG Cong-xuan,CHEN Zhen,XIONG Fan,LI Ming,GE Li-yue,CHEN Hao.Large displacement motion optical flow estimation with non-rigid dense patch matching[J].Acta Electronica Sinica,2019,47(6):1316-1323.(in Chinese).
[10] Simonyan K,Zisserman A.Very deep convolutional networks for large-scale image recognition[A].International Conference on Learning Representations[C].San Diego:ICLR,2015.1-14.
[11] Szegedy C,Liu W,Jia Y,Sermanet P,Reed S,Anguelov D,Rabinovich A.Going deeper with convolutions[A].International Conference on Computer Vision and Pattern Recognition[C].Boston:IEEE,2015.1-9.
[12] He K,Zhang X,Ren S,Sun J.Deep residual learning for image recognition[A].International Conference on Computer Vision and Pattern Recognition[C].Las Vegas:IEEE,2016.770-778.
[13] Huang G,Liu Z,Van DerMaaten L,Weinberger K Q.Densely connected convolutional networks[A].International Conference on Computer Vision and Pattern Recognition[C].Hawaii:IEEE,2017.4700-4708.
[14] Jung S,Hwang S,Shin H,Shim D H.Perception,guidance,and navigation for indoor autonomous drone racing using deep learning[J].IEEE Robotics and Automation Letters,2018,3(3):2539-2544.
[15] Gomaa A,Abdelwahab M M,Abo-Zahhad M,Minematsu T,Taniguchi R I.Robust vehicle detection and counting algorithm employing a convolution neural network and optical flow[J].Sensors,2019,19(20):4588.
[16] Zhu Z,Wu W,Zou W,Yan J.End-to-end flow correlation tracking with spatial-temporal attention[A].International Conference on Computer Vision and Pattern Recognition[C].Salt Lake City:IEEE,2018.548-557.
[17] Djelouah A,Campos J,Schaub-Meyer S,Schroers C.Neural inter-frame compression for video coding[A].International Conference on Computer Vision[C].Long Beach:IEEE,2019.6421-6429.
[18] Dosovitskiy A,Fischer P,Ilg E,Hausser P,Hazirbas C,Golkov V,Brox T.Flownet:Learning optical flow with convolutional networks[A].International Conference on Computer Vision and Pattern Recognition[C].Boston:IEEE,2015.2758-2766.
[19] Ronneberger O,Fischer P,Brox T.U-net:Convolutional networks for biomedical image segmentation[A].International Conference on Medical Image Computing and Computer-Assisted Intervention[C].Munich:Springer,2015.234-241.
[20] Ilg E,Mayer N,Saikia T,Dosovitskiy A,Brox T.FlowNet 2.0:Evolution of optical flow estimation with deep networks[A].International Conference on Computer Vision and Pattern Recognition[C].Hawaii:IEEE,2017.1647-1655.
[21] Ranjan A,Black M J.Optical flow estimation using a spatial pyramid network[A].International Conference on Computer Vision and Pattern Recognition[C].Hawaii:IEEE,2017.4161-4170.
[22] Sun D,Yang X,Liu M Y,Kautz J.PWC-Net:CNNs for optical flow using pyramid,warping,and cost volume[A].International Conference on Computer Vision and Pattern Recognition[C].Salt Lake City:IEEE,2018.8934-8943.
[23] Ilg E,Saikia T,Keuper M,Brox T.Occlusions,motion and depth boundaries with a generic network for disparity,optical flow or scene flow estimation[A].European Conference on Computer Vision[C].Munich:Springer,2018.614-630.
[24] Hui T W,Tang X,Loy CC.LiteFlowNet:A lightweight convolutional neural network for optical flow estimation[A].International Conference on Computer Vision and Pattern Recognition[C].Salt Lake City:IEEE,2018.8981-8989.
[25] Sevilla-Lara L,Sun D,Jampani V,Black M J.Optical flow with semantic segmentation and localized layers[A].International Conference on Computer Vision and Pattern Recognition[C].Las Vegas:IEEE,2016.3889-3898.
[26] Cheng J,Tsai Y H,Wang S,Yang M H.Segflow:Joint learning for video object segmentation and optical flow[A].International Conference on Computer Vision[C].Venice:IEEE,2017.686-695.
[27] Kingma D P,Ba J.Adam:A Method for Stochastic Optimization[EB/OL].http://arxiv.org/abs/1412.6980,2014.
[28] Jason J Y,Harley A W,Derpanis K G.Back to basics:Unsupervised learning of optical flow via brightness constancy and motion smoothness[A].European Conference on Computer Vision[C].Amsterdam:Springer,2016.3-10.
[29] Meister,Simon,Junhwa Hur,Stefan Roth.UnFlow:Unsupervised learning of optical flow with a bidirectional census loss[A].Thirty-Second AAAI Conference on Artificial Intelligence[C].Hawaii:Springer,2018.682-691.
[30] Wang Y,Yang Y,Yang Z,Zhao L,Wang P,Xu W.Occlusion aware unsupervised learning of optical flow[A].International Conference on Computer Vision and Pattern Recognition[C].Salt Lake City:IEEE,2018.4884-4893.
[31] Janai J,Guney F,Ranjan A,Black M,Geiger A.Unsupervised learning of multi-frame optical flow with occlusions[A].European Conference on Computer Vision[C].Munich:Springer,2018.690-706.
[32] Zhu A Z,Yuan L,Chaney K,Daniilidis K.Unsupervised event-based learning of optical flow,depth,and egomotion[A].International Conference on Computer Vision and Pattern Recognition[C].Long Beach:IEEE,2019.989-997.
[33] Paredes-Vallés F,Scheper K Y W,De Croon G C H E.Unsupervised learning of a hierarchical spiking neural network for optical flow estimation:From events to global motion perception[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,2(8):2051-2064.
[34] Shedligeri P,Mitra K.Live Demonstration:Joint estimation of optical flow and intensity image from event sensors[A].International Conference on Computer Vision and Pattern Recognition[C].Long Beach:IEEE,2019.1-2.
[35] Rasmus A,Berglund M,Honkala M,Valpola H,Raiko T.Semi-supervised learning with ladder networks[A].Advances in Neural Information Processing Systems[C].Montréal:MIT,2015.3546-3554.
[36] Zhu Y,Lan Z,Newsam S,Hauptmann A G.Guided optical flow learning[A].International Conference on Computer Vision and Pattern Recognition[C].Hawaii:IEEE,2017.1-5.
[37] Yang G,Deng Z,Wang S,Li Z.Masked label learning for optical flow regression[A].International Conference on Pattern Recognition[C].Beijing:IEEE,2018.1139-1144.
[38] Lai W S,Huang J B,Yang M H.Semi-supervised learning for optical flow with generative adversarial networks[A].Advances in Neural Information Processing Systems[C].Long Beach:MIT,2017.354-364.
[39] Weinzaepfel P,Revaud J,Harchaoui Z,Schmid C.Deepflow:large displacement optical flow with deep matching[A].International Conference on Computer Vision and Pattern Recognition[C].Portland:IEEE,2013.1385-1392.
[40] Hosni A,Rhemann C,Bleyer M,Rother C,Gelautz M.Fast cost-volume filtering for visual correspondence and beyond[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(2):504-511.
[41] Lin T Y,Dollár P,Girshick R,He K,Hariharan B,Belongie S.Feature pyramid networks for object detection[A].International Conference on Computer Vision and Pattern Recognition[C].Hawaii:IEEE,2017.2117-2125.
[42] Sun D,Roth S,Black M J.A quantitative analysis of current practices in optical flow estimation and the principles behind them[J].International Journal of Computer Vision,2014,106(2):115-137.
[43] Xu L,Jia J,Matsushita Y.Motion detail preserving optical flow estimation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,34(9):1744-1757.
[44] Zhang C,Ge L,Chen Z,et al.Refined TV-L1optical flow estimation using joint filtering[J].IEEE Transactions on Multimedia,2019.
[45] Revaud J,Weinzaepfel P,Harchaoui Z,Schmid C.Epicflow:edge-preserving interpolation of correspondences for optical flow[A].International Conference on Computer Vision and Pattern Recognition[C].Boston:IEEE,2015.1164-1172.
[46] Yu F,Koltun V.Multi-scale context aggregation by dilated convolutions[EB/OL].http://arxiv.org/abs/1511.07122V2,2015.
[47] Lai H Y,Tsai Y H,Chiu W C.Bridging stereo matching and optical flow via spatiotemporal correspondence[A].International Conference on Computer Vision and Pattern Recognition[C].Long Beach:IEEE,2019.1890-1899.
[48] Ranjan A,Jampani V,Balles L,Kim K,Sun D,Wulff J,Black M J.Competitive collaboration:joint unsupervised learning of depth,camera motion,optical flow and motion segmentation[A].International Conference on Computer Vision and Pattern Recognition[C].Long Beach:IEEE,2019.12240-12249.
[49] Yin Z,Shi J.Geonet:Unsupervised learning of dense depth,optical flow and camera pose[A].International Conference on Computer Vision and Pattern Recognition[C].Salt Lake City:IEEE,2018.1983-1992.
[50] Baker S,Scharstein D,Lewis J P,Roth S,Black M J,Szeliski R.A database and evaluation methodology for optical flow[J].International Journal of Computer Vision,2011,92(1):1-31.
[51] Butler D J,Wulff J,Stanley G B,Black M J.A naturalistic open source movie for optical flow evaluation[A].European Conference on Computer Vision[C].Florence:Springer,2012.611-625.
[52] Geiger A,Lenz P,Stiller C,Urtasun R.Vision meets robotics:The KITTI dataset[J].International Journal of Robotics Research,2013,32(11):1231-1237.
[53] Aubry M,Maturana D,Efros A A,Russell B C,Sivic J.Seeing 3D chairs:Exemplar part-based 2d-3d alignment using a large dataset of cad models[A].International Conference on Computer Vision and Pattern Recognition[C].Columbus:IEEE,2014.3762-3769.
[54] Mayer N,Ilg E,Häusser P,Cremers D,Dosovitskiy A,Brox T.A large dataset to train convolutional networks for disparity,optical flow,and scene flow estimation[A].International Conference on Computer Vision and Pattern Recognition[C].Las Vegas:IEEE,2016.4040-4048.
[55] Fleet D J,Jepson A D.Computation of component image velocity from local phase information[J].International Journal of Computer Vision,1990,5(1):77-104.
[56] Ilg E,Cicek O,Galesso S,Klein A,Makansi O,Hutter F,Brox T.Uncertainty estimates and multi-hypotheses networks for optical flow[A].European Conference on Computer Vision[C].Munich:Springer,2018.652-667. |