18.3.4.7 Spatio-Temporal Motion Segmentation, Flow Based Segmentation

Castellano, G.[Gabriela], Boyce, J.[James], Sandler, M.[Mark],
Regularized CDWT optical flow applied to moving-target detection in IR imagery,
MVA(11), No. 6, 2000, pp. 277-288.
Springer DOI 0005
BibRef
Earlier:
Moving Target Detection in Infrared Imagery Using a Regularised CDWT Optical Flow,
CVBVS99(13).
IEEE DOI 9906
BibRef

Sarkar, S.[Sudeep], Majchrzak, D.[Daniel], Korimilli, K.[Kishore],
Perceptual Organization Based Computational Model for Robust Segmentation of Moving Objects,
CVIU(86), No. 3, June 2002, pp. 141-170.
DOI Link 0301
BibRef
Earlier: A3, A1, Only:
Motion Segmentation Based on Perceptual Organization of Spatio-temporal Volumes,
ICPR00(Vol III: 844-849).
IEEE DOI 0009
BibRef

Zhang, X.P.[Xiao-Ping], Chen, Z.[Zhenhe],
An Automated Video Object Extraction System Based on Spatiotemporal Independent Component Analysis and Multiscale Segmentation,
JASP(2006), 2006, pp. 1-22.
WWW Link. 0603
BibRef

Dosil, R.[Raquel], Fdez-Vidal, X.R.[Xose R.], Pardo, X.M.[Xose M.],
Motion representation using composite energy features,
PR(41), No. 3, March 2008, pp. 1110-1123.
Elsevier DOI 0711

See also Distances between frequency features for 3D visual pattern partitioning. Spatio-temporal energy filtering, Feature integration; Composite energy features, Apparent-motion segmentation and tracking BibRef

Yang, H.[Hong], Tian, J.W.[Jin-Wen], Chu, Y., Tang, Q., Liu, J.[Jian],
Spatiotemporal Smooth Models for Moving Object Detection,
SPLetters(15), No. 1, 2008, pp. 497-500.
IEEE DOI 0806
BibRef

Li, Y.S.[Yan-Sheng], Tan, Y.H.[Yi-Hua], Yu, J.G.[Jin-Gang], Qi, S.X.[Sheng-Xiang], Tian, J.W.[Jin-Wen],
Kernel regression in mixed feature spaces for spatio-temporal saliency detection,
CVIU(135), No. 1, 2015, pp. 126-140.
Elsevier DOI 1504
Spatio-temporal saliency BibRef

Yang, H.[Hong], Tan, Y.H.[Yi-Hua], Tian, J.W.[Jin-Wen], Liu, J.[Jian],
Accurate Dynamic Scene Model for Moving Object Detection,
ICIP07(VI: 157-160).
IEEE DOI 0709
BibRef

Gryn, J.M.[Jacob M.], Wildes, R.P.[Richard P.], Tsotsos, J.K.[John K.],
Detecting Motion Patterns via Direction Maps with Application to Surveillance,
CVIU(113), No. 2, February 2009, pp. 291-307.
Elsevier DOI 0901
BibRef
And: WACV05(I: 202-209).
IEEE DOI 0502
Surveillance, Direction maps, Dominant direction, Event detection; Spatiotemporal analysis, Motion analysis BibRef

Guan, Y.P.,
Spatio-temporal motion-based foreground segmentation and shadow suppression,
IET-CV(4), No. 1, March 2010, pp. 50-60.
DOI Link 1001
BibRef

Luo, Y.[Yong], Guan, Y.P.[Ye Peng],
Motion objects segmentation based on structural similarity background modelling,
IET-CV(9), No. 4, 2015, pp. 476-488.
DOI Link 1509
computer vision BibRef

Wedel, A.[Andreas], Brox, T.[Thomas], Vaudrey, T.[Tobi], Rabe, C.[Clemens], Franke, U.[Uwe], Cremers, D.[Daniel],
Stereoscopic Scene Flow Computation for 3D Motion Understanding,
IJCV(95), No. 1, October 2011, pp. 29-51.
WWW Link. 1108
BibRef
Earlier: A1, A4, A3, A2, A5, A6:
Efficient Dense Scene Flow from Sparse or Dense Stereo Data,
ECCV08(I: 739-751).
Springer DOI 0810

See also Video Super Resolution Using Duality Based TV-L1 Optical Flow. BibRef

Ochs, P.[Peter], Malik, J.[Jitendra], Brox, T.[Thomas],
Segmentation of Moving Objects by Long Term Video Analysis,
PAMI(36), No. 6, June 2014, pp. 1187-1200.
IEEE DOI 1406
BibRef
Earlier: A3, A2, Only:
Object Segmentation by Long Term Analysis of Point Trajectories,
ECCV10(V: 282-295).
Springer DOI 1009
BibRef
And: A1, A3, Only:
Higher order motion models and spectral clustering,
CVPR12(614-621).
IEEE DOI 1208
BibRef
And: A1, A3, Only:
Object segmentation in video: A hierarchical variational approach for turning point trajectories into dense regions,
ICCV11(1583-1590).
IEEE DOI 1201
Adaptive optics BibRef

Quiroga, J.[Julian], Brox, T.[Thomas], Devernay, F.[Frédéric], Crowley, J.L.[James L.],
Dense Semi-rigid Scene Flow Estimation from RGBD Images,
ECCV14(VII: 567-582).
Springer DOI 1408
BibRef

Xu, J.[Jiang], Yuan, J.S.[Jun-Song], Wu, Y.[Ying],
Learning spatio-temporal dependency of local patches for complex motion segmentation,
CVIU(115), No. 3, March 2011, pp. 334-351.
Elsevier DOI 1103
Motion segmentation, Learning, Motion profile symmetry correlation; Bipolar segmentation BibRef

Wedel, A.[Andreas], Cremers, D.[Daniel],
Stereo Scene Flow for 3D Motion Analysis,
Springer2011. ISBN: 978-0-85729-964-2.
WWW Link. 1109
BibRef

Wedel, A.[Andreas], Pock, T., Braun, J., Franke, U., Cremers, D.[Daniel],
Duality TV-L1 flow with fundamental matrix prior,
IVCNZ08(1-6).
IEEE DOI 0811
BibRef

Klappstein, J.[Jens], Vaudrey, T.[Tobi], Rabe, C.[Clemens], Wedel, A.[Andreas], Klette, R.[Reinhard],
Moving Object Segmentation Using Optical Flow and Depth Information,
PSIVT09(611-623).
Springer DOI 0901
BibRef
Earlier: A2, A4, A3, A1, A5:
Evaluation of moving object segmentation comparing 6D-vision and monocular motion constraints,
IVCNZ08(1-6).
IEEE DOI 0811
BibRef

Wedel, A.[Andreas], Meißner, A.[Annemarie], Rabe, C.[Clemens], Franke, U.[Uwe], Cremers, D.[Daniel],
Detection and Segmentation of Independently Moving Objects from Dense Scene Flow,
EMMCVPR09(14-27).
Springer DOI 0908
BibRef

Klappstein, J.[Jens], Stein, F.[Fridtjof], Franke, U.[Uwe],
Detectability of Moving Objects Using Correspondences over Two and Three Frames,
DAGM07(112-121).
Springer DOI 0709
BibRef

Wu, B.[Bo], Xu, L.F.[Lin-Feng], Zeng, L.Y.[Liao-Yuan], Wang, Z.N.[Zheng-Ning], Wang, Y.[Yan],
A unified framework for spatiotemporal salient region detection,
JIVP(2013), No. 1, 2013, pp. 16.
DOI Link 1305
BibRef

Li, R.N.[Ruo-Nan], Chellappa, R.[Rama],
Spatiotemporal Alignment of Visual Signals on a Special Manifold,
PAMI(35), No. 3, March 2013, pp. 697-715.
IEEE DOI 1303
BibRef
Earlier:
Aligning Spatio-Temporal Signals on a Special Manifold,
ECCV10(V: 547-560).
Springer DOI 1009
BibRef
And:
Group motion segmentation using a Spatio-Temporal Driving Force Model,
CVPR10(2038-2045).
IEEE DOI 1006
BibRef

Belardinelli, A.[Anna], Carbone, A.[Andrea], Schneider, W.X.[Werner X.],
Classification of multiscale spatiotemporal energy features for video segmentation and dynamic objects prioritisation,
PRL(34), No. 7, 1 May 2013, pp. 713-722.
Elsevier DOI 1303
Video segmentation, Spatiotemporal features, Visual attention; Object-based saliency BibRef

Wang, W.G.[Wen-Guan], Shen, J.B.[Jian-Bing], Shao, L.[Ling],
Consistent Video Saliency Using Local Gradient Flow Optimization and Global Refinement,
IP(24), No. 11, November 2015, pp. 4185-4196.
IEEE DOI 1509
gradient methods BibRef

Wang, W.G.[Wen-Guan], Shen, J.B.[Jian-Bing], Shao, L.[Ling],
Video Salient Object Detection via Fully Convolutional Networks,
IP(27), No. 1, January 2018, pp. 38-49.
IEEE DOI 1712
convolution, image annotation, image segmentation, image sequences, inference mechanisms, learning (artificial intelligence), synthetic video data BibRef

Wang, W.G.[Wen-Guan], Shen, J.B.[Jian-Bing], Sun, H., Shao, L.[Ling],
Video Co-Saliency Guided Co-Segmentation,
CirSysVideo(28), No. 8, August 2018, pp. 1727-1736.
IEEE DOI 1808
Visualization, Silicon, Video sequences, Estimation, Motion segmentation, Proposals, Circuits and systems, video co-segmentation BibRef

Wang, W.G.[Wen-Guan], Shen, J.B.[Jian-Bing], Shao, L.[Ling], Porikli, F.M.[Fatih Murat],
Correspondence Driven Saliency Transfer,
IP(25), No. 11, November 2016, pp. 5025-5034.
IEEE DOI 1610
estimation theory. BibRef

Wang, W.G.[Wen-Guan], Shen, J.B.[Jian-Bing], Yang, R., Porikli, F.M.[Fatih Murat],
Saliency-Aware Video Object Segmentation,
PAMI(40), No. 1, January 2018, pp. 20-33.
IEEE DOI 1712
BibRef
Earlier: A1, A2, A4, Only:
Saliency-aware geodesic video object segmentation,
CVPR15(3395-3402)
IEEE DOI 1510
Image segmentation, Motion segmentation, Object segmentation, Proposals, Skeleton, Spatiotemporal phenomena, Trajectory, video object segmentation BibRef

Wang, W.G.[Wen-Guan], Shen, J.B.[Jian-Bing], Xie, J., Porikli, F.M.[Fatih Murat],
Super-Trajectory for Video Segmentation,
ICCV17(1680-1688)
IEEE DOI 1802
image motion analysis, image representation, image segmentation, pattern clustering, spatiotemporal phenomena, Video sequences BibRef

Wang, W.G.[Wen-Guan], Shen, J.B.[Jian-Bing], Li, X.L.[Xue-Long], Porikli, F.M.[Fatih M.],
Robust Video Object Cosegmentation,
IP(24), No. 10, October 2015, pp. 3137-3148.
IEEE DOI 1507
feature extraction BibRef

Wang, W.G.[Wen-Guan], Shen, J.B.[Jian-Bing], Porikli, F.M.[Fatih M.],
Selective Video Object Cutout,
IP(26), No. 12, December 2017, pp. 5645-5655.
IEEE DOI 1710
pyramid histogram-based confidence map, structure information, uncertainty propagation, BibRef

Ortego, D.[Diego], San Miguel, J.C.[Juan C.], Martínez, J.M.[José M.],
Long-Term Stationary Object Detection Based on Spatio-Temporal Change Detection,
SPLetters(22), No. 12, December 2015, pp. 2368-2372.
IEEE DOI 1512
feature extraction BibRef

Chen, C.Z.[Chengli-Zhao], Li, S.[Shuai], Qin, H.[Hong], Hao, A.[Aimin],
Robust salient motion detection in non-stationary videos via novel integrated strategies of spatio-temporal coherency clues and low-rank analysis,
PR(52), No. 1, 2016, pp. 410-432.
Elsevier DOI 1601
Salient motion detection BibRef

Chen, C.Z.[Chengli-Zhao], Li, S.[Shuai], Wang, Y., Qin, H.[Hong], Hao, A.[Aimin],
Video Saliency Detection via Spatial-Temporal Fusion and Low-Rank Coherency Diffusion,
IP(26), No. 7, July 2017, pp. 3156-3170.
IEEE DOI 1706
Cameras, Computational modeling, Feature extraction, Image color analysis, Motion detection, Robustness, Video sequences, Spatial-temporal saliency fusion, low-rank coherency guided saliency diffusion, video saliency, visual, saliency BibRef

Chen, C.Z.[Chengli-Zhao], Li, Y., Li, S.[Shuai], Qin, H.[Hong], Hao, A.[Aimin],
A Novel Bottom-Up Saliency Detection Method for Video With Dynamic Background,
SPLetters(25), No. 2, February 2018, pp. 154-158.
IEEE DOI 1802
cameras, filtering theory, image motion analysis, image sequences, object detection, video signal processing, video saliency BibRef

Galteri, L., Seidenari, L.[Lorenzo], Bertini, M., del Bimbo, A.[Alberto],
Spatio-Temporal Closed-Loop Object Detection,
IP(26), No. 3, March 2017, pp. 1253-1263.
IEEE DOI 1703
computer vision BibRef

Cuffaro, G.[Giovanni], Becattini, F.[Federico], Baecchi, C.[Claudio], Seidenari, L.[Lorenzo], del Bimbo, A.[Alberto],
Segmentation Free Object Discovery in Video,
MotionRep16(III: 25-31).
Springer DOI 1611
BibRef

Spampinato, C.[Concetto], Palazzo, S.[Simone], Giordano, D.[Daniela],
Gamifying Video Object Segmentation,
PAMI(39), No. 10, October 2017, pp. 1942-1958.
IEEE DOI 1709
BibRef
Earlier: A1, A2, Only:
Enhancing object detection performance by integrating motion objectness and perceptual organization,
ICPR12(3640-3643).
WWW Link. 1302
is the blob an object of interest.. Data mining, Games, Motion segmentation, Object segmentation, Visualization, Interactive video annotation, games with a purpose, human in the loop, spatio-temporal, superpixel segmentation BibRef

Giordano, D.[Daniela], Kavasidis, I.[Isaak], Palazzo, S.[Simone], Spampinato, C.[Concetto],
Rejecting False Positives in Video Object Segmentation,
CAIP15(I:100-112).
Springer DOI 1511
BibRef

Tu, Z.G.[Zhi-Gang], Guo, Z.[Zuwei], Xie, W.[Wei], Yan, M.J.[Meng-Jia], Veltkamp, R.C.[Remco C.], Li, B.X.[Bao-Xin], Yuan, J.S.[Jun-Song],
Fusing disparate object signatures for salient object detection in video,
PR(72), No. 1, 2017, pp. 285-299.
Elsevier DOI 1708
Spatiotemporal saliency computation BibRef

Ray, K.S.[Kumar S.], Chakraborty, S.[Soma],
Object detection by spatio-temporal analysis and tracking of the detected objects in a video with variable background,
JVCIR(58), 2019, pp. 662-674.
Elsevier DOI 1901
Variable background, Object detection, Gabor filter, Spatio-temporal analysis, Minimum Spanning Tree (MST), Occlusion BibRef

Peng, Y.X.[Yu-Xin], Zhao, Y.Z.[Yun-Zhen], Zhang, J.C.[Jun-Chao],
Two-Stream Collaborative Learning With Spatial-Temporal Attention for Video Classification,
CirSysVideo(29), No. 3, March 2019, pp. 773-786.
IEEE DOI 1903
The static frame and optical flow. Feature extraction, Adaptation models, Video sequences, Collaboration, Semantics, Collaborative work, Weapons, adaptively weighted learning BibRef

Tashlinskii, A.G., Smirnov, P.V., Tsaryov, M.G.,
Pixel-by-pixel Estimation of Scene Motion in Video,
PTVSBB17(61-65).
DOI Link 1805
BibRef

Erokhin, D.Y., Feldman, A.B., Korepanov, S.E.,
Detection And Tracking of Moving Objects with Real-time Onboard Vision System,
PTVSBB17(67-71).
DOI Link 1805
BibRef

Haque, N.[Nazrul], Reddy, N.D.[N. Dinesh], Krishna, M.[Madhava],
Temporal Semantic Motion Segmentation Using Spatio Temporal Optimization,
EMMCVPR17(93-108).
Springer DOI 1805
BibRef

Li, J., Zheng, A., Chen, X., Zhou, B.,
Primary Video Object Segmentation via Complementary CNNs and Neighborhood Reversible Flow,
ICCV17(1426-1434)
IEEE DOI 1802
convolution, image segmentation, neural nets, video signal processing, Complementary CNNs, Training BibRef

Taniai, T., Sinha, S.N., Sato, Y.,
Fast Multi-frame Stereo Scene Flow with Motion Segmentation,
CVPR17(6891-6900)
IEEE DOI 1711
Cameras, Motion segmentation, Optical imaging, BibRef

Du, Y., Yuan, C., Li, B., Hu, W., Maybank, S.J.[Steve J.],
Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection from Videos,
CVPR17(4245-4254)
IEEE DOI 1711
Bayes methods, Dynamics, Kernel, Object detection, Self-organizing feature maps, Videos BibRef

Cheng, J., Tsai, Y.H.[Yi-Hsuan], Wang, S., Yang, M.H.[Ming-Hsuan],
SegFlow: Joint Learning for Video Object Segmentation and Optical Flow,
ICCV17(686-695)
IEEE DOI 1802
image segmentation, image sequences, learning (artificial intelligence), video signal processing, Training BibRef

Tsai, Y.H.[Yi-Hsuan], Yang, M.H.[Ming-Hsuan], Black, M.J.[Michael J.],
Video Segmentation via Object Flow,
CVPR16(3899-3908)
IEEE DOI 1612
BibRef

Hur, J.[Junhwa], Roth, S.[Stefan],
Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation,
CVPR19(5747-5756).
IEEE DOI 2002
BibRef
Earlier:
MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation,
ICCV17(312-321)
IEEE DOI 1802
BibRef
Earlier:
Joint Optical Flow and Temporally Consistent Semantic Segmentation,
CVRoads16(I: 163-177).
Springer DOI 1611
image sequences, inference mechanisms, optimisation, MirrorFlow, chicken-and-egg relation, forward-backward consistency, Transforms BibRef

Teney, D.[Damien], Brown, M.[Matthew],
Segmentation of Dynamic Scenes with Distributions of Spatiotemporally Oriented Energies,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Mukherjee, D.[Dibyendu], Wu, Q.M.J.[Q.M. Jonathan],
Streaming spatio-temporal video segmentation using Gaussian Mixture Model,
ICIP14(4388-4392)
IEEE DOI 1502
Clustering algorithms BibRef

Morimitsu, H.[Henrique], Cesar, Jr., R.M.[Roberto M.], Bloch, I.[Isabelle],
A Spatio-temporal Approach for Multiple Object Detection in Videos Using Graphs and Probability Maps,
ICIAR14(II: 421-428).
Springer DOI 1410
BibRef

Oneata, D.[Dan], Revaud, J.[Jerome], Verbeek, J.[Jakob], Schmid, C.[Cordelia],
Spatio-temporal Object Detection Proposals,
ECCV14(III: 737-752).
Springer DOI 1408
The bounding boxes to use in event detection.
See also Action and Event Recognition with Fisher Vectors on a Compact Feature Set. BibRef

Muddamsetty, S.M.[Satya M.], Sidibe, D.[Desire], Tremeau, A.[Alain], Meriaudeau, F.[Fabrice],
Spatio-temporal Saliency Detection in Dynamic Scenes Using Local Binary Patterns,
ICPR14(2353-2358)
IEEE DOI 1412
BibRef
Earlier:
A performance evaluation of fusion techniques for spatio-temporal saliency detection in dynamic scenes,
ICIP13(3924-3928)
IEEE DOI 1402
Computational modeling. Spatio-temporal saliency BibRef

Nawaf, M.M.[Mohamad Motasem], Hasnat, M.A.[M. Abul], Sidibe, D.[Desire], Tremeau, A.[Alain],
Color and flow based superpixels for 3D geometry respecting meshing,
WACV14(153-158)
IEEE DOI 1406
Adaptive optics BibRef

Cheng, H.T.T.[Hsien-Ting Tim], Ahuja, N.[Narendra],
Exploiting nonlocal spatiotemporal structure for video segmentation,
CVPR12(741-748).
IEEE DOI 1208
BibRef

Xie, D.F.[Dan-Feng], Huang, Z.W.[Zhi-Wei], Wang, S.Z.[Shi-Zheng], Liu, H.G.[He-Guang],
Moving Objects Segmentation from compressed surveillance video based on Motion Estimation,
ICPR12(3132-3135).
WWW Link. 1302
BibRef

Yang, J.W.[Jian-Wei], Wang, S.Z.[Shi-Zheng], Lei, Z.[Zhen], Zhao, Y.Y.[Yan-Yun], Li, S.Z.[Stan Z.],
Spatio-temporal LBP Based Moving Object Segmentation in Compressed Domain,
AVSS12(252-257).
IEEE DOI 1211
BibRef

Lalos, C.[Constantinos], Grabner, H.[Helmut], Van Gool, L.J.[Luc J.], Varvarigou, T.A.[Theodora A.],
Object Flow: Learning Object Displacement,
VS10(133-142).
Springer DOI 1109
BibRef

Zhao, H.[Hui], Sun, X.J.[Xiang-Ju], Meng, C.H.[Cai-Hong],
Moving object detection based on T-test combined with kirsch operator,
IASP11(199-203).
IEEE DOI 1112
BibRef

Gao, P.[Ping], Sun, X.J.[Xiang-Ju], Wang, W.[Wei],
Moving object detection based on Kirsch operator combined with Optical Flow,
IASP10(620-624).
IEEE DOI 1004
BibRef

Qin, X.K.[Xiao-Ke], Tang, L.[Liang], Zhou, J.[Jie],
Object Extraction by Spatio-Temporal Assembling,
ICIP07(V: 153-156).
IEEE DOI 0709
BibRef

Galmar, E.[Eric], Huet, B.[Benoit],
Graph-Based Spatio-temporal Region Extraction,
ICIAR06(I: 236-247).
Springer DOI 0610
BibRef

Kaempchen, N.[Nico], Zocholl, M.[Markus], Dietmayer, K.C.J.[Klaus C.J.],
Spatio-temporal Segmentation Using Laser-scanner and Video Sequences,
DAGM04(367-374).
Springer DOI 0505
BibRef

Yip, H.M.[Hoi-Man], Pong, T.C.[Ting-Chuen],
Detection of moving objects using a spatiotemporal representation,
ICPR96(I: 483-487).
IEEE DOI 9608
(The Hong Kong Univ., HK) BibRef

James, P.D., Spann, M.,
Multiresolution Motion Estimation/Segmentation Incorporating Feature Correspondence and Optical Flow,
BMVC95(xx-yy).
PDF File. 9509
BibRef

Hirai, T., Sasakawa, K., Kuroda, S., Ikebata, S.,
Detection of small moving object by optical flow,
ICPR92(II:474-478).
IEEE DOI 9208
BibRef

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Motion Segmentation by Tracking, Trajectories, Region Based Tracking .