Surveillance of Crowded Environments: Modeling the Crowd by Its Global Properties
暂无分享,去创建一个
[1] Mubarak Shah,et al. A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[2] Mubarak Shah,et al. A Streakline Representation of Flow in Crowded Scenes , 2010, ECCV.
[3] Stefano Soatto,et al. Dynamic Textures , 2003, International Journal of Computer Vision.
[4] Ramakant Nevatia,et al. Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[5] Roberto Cipolla,et al. Unsupervised Bayesian Detection of Independent Motion in Crowds , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[6] Stefano Soatto,et al. Spatially Homogeneous Dynamic Textures , 2004, ECCV.
[7] Dani Lischinski,et al. Texture Mixing and Texture Movie Synthesis Using Statistical Learning , 2001, IEEE Trans. Vis. Comput. Graph..
[8] René Vidal,et al. A System Theoretic Approach to Synthesis and Classification of Lip Articulation , 2007 .
[9] Richard J. Martin. A metric for ARMA processes , 2000, IEEE Trans. Signal Process..
[10] Narendra Ahuja,et al. Dynamic Textures Synthesis as Nonlinear Manifold Learning and Traversing , 2006, BMVC.
[11] Takeo Kanade,et al. An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.
[12] Antoni B. Chan,et al. Generalized Gaussian process models , 2011, CVPR 2011.
[13] Paul A. Viola,et al. Detecting Pedestrians Using Patterns of Motion and Appearance , 2005, International Journal of Computer Vision.
[14] Narendra Ahuja,et al. Phase Based Modelling of Dynamic Textures , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[15] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[16] Stan Sclaroff,et al. Segmenting foreground objects from a dynamic textured background via a robust Kalman filter , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[17] Nikos Paragios,et al. A MRF-based approach for real-time subway monitoring , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[18] Yandong Tang,et al. Flow mosaicking: Real-time pedestrian counting without scene-specific learning , 2009, CVPR.
[19] Nuno Vasconcelos,et al. Probabilistic kernels for the classification of auto-regressive visual processes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[20] David A. McAllester,et al. A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[21] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[22] Nuno Vasconcelos,et al. Analysis of Crowded Scenes using Holistic Properties , 2009 .
[23] René Vidal,et al. Online Clustering of Moving Hyperplanes , 2006, NIPS.
[24] Jun Liu,et al. Spatial Segmentation of Temporal Texture Using Mixture Linear Models , 2006, WDV.
[25] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[26] S. Kay. Fundamentals of statistical signal processing: estimation theory , 1993 .
[27] David J. Fleet,et al. Performance of optical flow techniques , 1994, International Journal of Computer Vision.
[28] Martin Szummer,et al. Temporal texture modeling , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.
[29] Stefano Soatto,et al. Recognition of human gaits , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[30] Nuno Vasconcelos,et al. Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[31] Nuno Vasconcelos,et al. Anomaly detection in crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[32] David J. Fleet,et al. Performance of optical flow techniques , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[33] Nuno Vasconcelos,et al. Classifying Video with Kernel Dynamic Textures , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[34] R. Shumway,et al. AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM , 1982 .
[35] Arthur Gelb,et al. Applied Optimal Estimation , 1974 .
[36] Dietmar Bauer,et al. Comparing the CCA Subspace Method to Pseudo Maximum Likelihood Methods in the case of No Exogenous Inputs , 2005 .
[37] B. De Moor,et al. Subspace angles between linear stochastic models , 2000, CDC 2000.
[38] Berthold K. P. Horn. Robot vision , 1986, MIT electrical engineering and computer science series.
[39] Nuno Vasconcelos,et al. Counting People With Low-Level Features and Bayesian Regression , 2012, IEEE Transactions on Image Processing.
[40] Wallace E. Larimore,et al. Canonical variate analysis in identification, filtering, and adaptive control , 1990, 29th IEEE Conference on Decision and Control.
[41] Bernt Schiele,et al. Pedestrian detection in crowded scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[42] Nuno Vasconcelos,et al. Layered Dynamic Textures , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[43] Berthold K. P. Horn,et al. Determining Optical Flow , 1981, Other Conferences.
[44] Nuno Vasconcelos,et al. Spatiotemporal Saliency in Dynamic Scenes , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[45] René Vidal,et al. Optical flow estimation & segmentation of multiple moving dynamic textures , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[46] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[47] Nuno Vasconcelos,et al. Bayesian Poisson regression for crowd counting , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[48] Sheng-Fuu Lin,et al. Estimation of number of people in crowded scenes using perspective transformation , 2001, IEEE Trans. Syst. Man Cybern. Part A.
[49] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[50] Sergio A. Velastin,et al. Crowd monitoring using image processing , 1995 .
[51] Ramin Mehran,et al. Abnormal crowd behavior detection using social force model , 2009, CVPR.
[52] Michael I. Jordan,et al. Multiple kernel learning, conic duality, and the SMO algorithm , 2004, ICML.
[53] Daniel Cremers,et al. Dynamic texture segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[54] A. Marana,et al. On the efficacy of texture analysis for crowd monitoring , 1998, Proceedings SIBGRAPI'98. International Symposium on Computer Graphics, Image Processing, and Vision (Cat. No.98EX237).
[55] Stefano Soatto,et al. Dynamic Shape and Appearance Models , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[56] Nello Cristianini,et al. Learning the Kernel Matrix with Semidefinite Programming , 2002, J. Mach. Learn. Res..
[57] Tommy W. S. Chow,et al. A neural-based crowd estimation by hybrid global learning algorithm , 1999, IEEE Trans. Syst. Man Cybern. Part B.
[58] Mubarak Shah,et al. Video Scene Understanding Using Multi-scale Analysis , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[59] Bart De Moor,et al. N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..
[60] A. Fitzgibbon. Stochastic rigidity: image registration for nowhere-static scenes , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[61] Sabine Süsstrunk,et al. Higher Order SVD Analysis for Dynamic Texture Synthesis , 2008, IEEE Transactions on Image Processing.
[62] Nuno Vasconcelos,et al. Privacy preserving crowd monitoring: Counting people without people models or tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[63] Gregory D. Hager,et al. Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions , 2009, CVPR.
[64] René Vidal,et al. View-invariant dynamic texture recognition using a bag of dynamical systems , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[65] Luciano da Fontoura Costa,et al. Estimating crowd density with Minkowski fractal dimension , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[66] Nikos Paragios,et al. Background modeling and subtraction of dynamic scenes , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[67] Mubarak Shah,et al. Learning motion patterns in crowded scenes using motion flow field , 2008, 2008 19th International Conference on Pattern Recognition.
[68] René Vidal,et al. Video Registration Using Dynamic Textures , 2011, IEEE Trans. Pattern Anal. Mach. Intell..
[69] Antoni B. Chan. Beyond dynamic textures : a family of stochastic dynamical models for video with applications to computer vision , 2008 .
[70] Andrew W. Fitzgibbon,et al. Shift-Invariant Dynamic Texture Recognition , 2006, ECCV.
[71] Michael Isard,et al. CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.
[72] Luc Van Gool,et al. Coupled Detection and Trajectory Estimation for Multi-Object Tracking , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[73] René Vidal,et al. Segmenting Dynamic Textures with Ising Descriptors, ARX Models and Level Sets , 2006, WDV.
[74] Mubarak Shah,et al. Scene understanding by statistical modeling of motion patterns , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[75] Mubarak Shah,et al. Detecting global motion patterns in complex videos , 2008, 2008 19th International Conference on Pattern Recognition.
[76] Visvanathan Ramesh,et al. Fast Crowd Segmentation Using Shape Indexing , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[77] Serge J. Belongie,et al. Counting Crowded Moving Objects , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[78] René Vidal,et al. DynamicBoost: Boosting Time Series Generated by Dynamical Systems , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[79] Edward H. Adelson,et al. Representing moving images with layers , 1994, IEEE Trans. Image Process..
[80] Ramakant Nevatia,et al. Bayesian human segmentation in crowded situations , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[81] Andrew Zisserman,et al. Learning To Count Objects in Images , 2010, NIPS.
[82] Harry Shum,et al. Synthesizing Dynamic Texture with Closed-Loop Linear Dynamic System , 2004, ECCV.
[83] Byron Boots,et al. A Constraint Generation Approach to Learning Stable Linear Dynamical Systems , 2007, NIPS.
[84] Alexander J. Smola,et al. Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes , 2007, International Journal of Computer Vision.
[85] Randal C. Nelson,et al. Recognition of motion from temporal texture , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[86] Hai Tao,et al. Counting Pedestrians in Crowds Using Viewpoint Invariant Training , 2005, BMVC.
[87] Carlo S. Regazzoni,et al. Distributed data fusion for real-time crowding estimation , 1996, Signal Process..
[88] Payam Saisan,et al. Dynamic texture recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[89] Nuno Vasconcelos,et al. Variational layered dynamic textures , 2009, CVPR.
[90] BlakeAndrew,et al. C ONDENSATION Conditional Density Propagation forVisual Tracking , 1998 .
[91] Nuno Vasconcelos,et al. Generalized Stauffer–Grimson background subtraction for dynamic scenes , 2011, Machine Vision and Applications.