Bayesian visual surveillance : from object detection to distributed cameras

Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.

[1]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[2]  Keiji Kanazawa,et al.  A model for reasoning about persistence and causation , 1989 .

[3]  Tom Heskes,et al.  Approximate Expectation Maximization , 2003, NIPS.

[4]  Jie Wei,et al.  Illumination-invariant color object recognition via compressed chromaticity histograms of color-channel-normalized images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[5]  Gregory D. Hager,et al.  Efficient Region Tracking With Parametric Models of Geometry and Illumination , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Ben J. A. Kröse,et al.  A hybrid graphical model for robust feature extraction from video , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  William T. Freeman,et al.  Learning low-level vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[8]  Michael Isard,et al.  Learning to Track the Visual Motion of Contours , 1995, Artif. Intell..

[9]  Brendan J. Frey,et al.  Factor graphs and the sum-product algorithm , 2001, IEEE Trans. Inf. Theory.

[10]  Robert J. McEliece,et al.  The generalized distributive law , 2000, IEEE Trans. Inf. Theory.

[11]  Mubarak Shah,et al.  View-Invariant Representation and Recognition of Actions , 2002, International Journal of Computer Vision.

[12]  Geoffrey E. Hinton,et al.  A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.

[13]  Stuart J. Russell,et al.  Image Segmentation in Video Sequences: A Probabilistic Approach , 1997, UAI.

[14]  Andrew Blake,et al.  Statistical Foreground Modelling for Object Localisation , 2000, ECCV.

[15]  Y. Bar-Shalom Tracking and data association , 1988 .

[16]  William T. Freeman,et al.  Understanding belief propagation and its generalizations , 2003 .

[17]  Lawrence D. Brown Fundamentals of Statistical Exponential Families , 1987 .

[18]  William T. Freeman,et al.  Constructing free-energy approximations and generalized belief propagation algorithms , 2005, IEEE Transactions on Information Theory.

[19]  Takashi Matsuyama,et al.  Real-time cooperative multi-target tracking by communicating active vision agents , 2002, Object recognition supported by user interaction for service robots.

[20]  Carl E. Rasmussen,et al.  Factorial Hidden Markov Models , 1997 .

[21]  Zoubin Ghahramani,et al.  An Introduction to Hidden Markov Models and Bayesian Networks , 2001, Int. J. Pattern Recognit. Artif. Intell..

[22]  David J. Kriegman,et al.  Tracking humans using prior and learned representations of shape and appearance , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[23]  John M. Winn,et al.  Variational Message Passing and its Applications , 2004 .

[24]  Jitendra Malik,et al.  Robust Multiple Car Tracking with Occlusion Reasoning , 1994, ECCV.

[25]  Alan L. Yuille,et al.  CCCP Algorithms to Minimize the Bethe and Kikuchi Free Energies: Convergent Alternatives to Belief Propagation , 2002, Neural Computation.

[26]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[27]  Dariu Gavrila,et al.  Virtual sample generation for template-based shape matching , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[28]  Jeff A. Bilmes,et al.  A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .

[29]  Kevin Murphy,et al.  Switching Kalman Filters , 1998 .

[30]  Michael I. Jordan,et al.  Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..

[31]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[32]  Ramin Zabih,et al.  Bayesian multi-camera surveillance , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[33]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[34]  Yaacov Ritov,et al.  Tracking Many Objects with Many Sensors , 1999, IJCAI.

[35]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[36]  Ingemar J. Cox,et al.  A review of statistical data association techniques for motion correspondence , 1993, International Journal of Computer Vision.

[37]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

[38]  L. Davis,et al.  el-based tracking of humans in action: , 1996 .

[39]  Ben Kröse,et al.  Gaussian Mixture Model for Multi-sensor Tracking , 2003 .

[40]  Michael Isard,et al.  Bayesian Object Localisation in Images , 2001, International Journal of Computer Vision.

[41]  Michael I. Jordan Learning in Graphical Models , 1999, NATO ASI Series.

[42]  Ingemar J. Cox,et al.  An efficient implementation and evaluation of Reid's multiple hypothesis tracking algorithm for visual tracking , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[43]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[44]  J. K. Aggarwal,et al.  Tracking human motion in indoor environments using a distributed-camera system , 1997 .

[45]  Tom Minka,et al.  Expectation-Propogation for the Generative Aspect Model , 2002, UAI.

[46]  Zoubin Ghahramani,et al.  A Unifying Review of Linear Gaussian Models , 1999, Neural Computation.

[47]  Daniel D. Lee,et al.  Learning a Continuous Hidden Variable Model for Binary Data , 1998, NIPS.

[48]  Tom Minka,et al.  Expectation Propagation for approximate Bayesian inference , 2001, UAI.

[49]  Andrew Blake,et al.  A Probabilistic Background Model for Tracking , 2000, ECCV.

[50]  Juan Ruiz-Alzola,et al.  Model-based stereo-visual tracking: Covariance analysis and tracking schemes , 2000, Signal Process..

[51]  P.J. Withagen,et al.  CCD characterization for a range of color cameras , 2005, 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings.

[52]  Yaakov Bar-Shalom,et al.  Estimation and Tracking: Principles, Techniques, and Software , 1993 .

[53]  Mubarak Shah,et al.  Tracking across multiple cameras with disjoint views , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[54]  Svetha Venkatesh,et al.  Tracking and Surveillance in Wide-Area Spatial Environments Using the Abstract Hidden Markov Model , 2001, Int. J. Pattern Recognit. Artif. Intell..

[55]  Carl E. Rasmussen,et al.  The Infinite Gaussian Mixture Model , 1999, NIPS.

[56]  Michael E. Tipping Probabilistic Visualisation of High-Dimensional Binary Data , 1998, NIPS.

[57]  Ann E. Nicholson,et al.  Dynamic Belief Networks for Discrete Monitoring , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[58]  B.J.A. Kröse,et al.  Bayesian Network for multiple hypthesis tracking , 2002 .

[59]  Ben J. A. Kröse,et al.  Keeping Track of Humans: Have I Seen This Person Before? , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[60]  Yi Li,et al.  A relaxation algorithm for real-time multiple view 3D-tracking , 2002, Image Vis. Comput..

[61]  T. Heskes,et al.  Expectation propagation for approximate inference in dynamic bayesian networks , 2002, UAI 2002.

[62]  Yee Whye Teh,et al.  Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation , 2001, UAI.

[63]  Nikos Vlassis,et al.  Bayesian methods for tracking and localization , 2006 .

[64]  Junji Yamato,et al.  Recognizing human action in time-sequential images using hidden Markov model , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[65]  Brendan J. Frey,et al.  Epitomic analysis of appearance and shape , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[66]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[67]  Ben J. A. Kröse,et al.  Online multicamera tracking with a switching state-space model , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[68]  Jake K. Aggarwal,et al.  Tracking Human Motion in Structured Environments Using a Distributed-Camera System , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[69]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[70]  Thomas P. Minka,et al.  The EP energy function and minimization schemes , 2001 .

[71]  S. J. Press,et al.  Applied Multivariate Analysis. , 1973 .

[72]  Zoran Zivkovic,et al.  Improved adaptive Gaussian mixture model for background subtraction , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[73]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[74]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[75]  Brendan J. Frey,et al.  A comparison of algorithms for inference and learning in probabilistic graphical models , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[76]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine-mediated learning.

[77]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[78]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[79]  Tommi S. Jaakkola,et al.  Tutorial on variational approximation methods , 2000 .

[80]  Yuan Qi,et al.  Expectation propagation for signal detection in flat-fading channels , 2003, IEEE International Symposium on Information Theory, 2003. Proceedings..

[81]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[82]  Stuart J. Russell,et al.  Object Identification: A Bayesian Analysis with Application to Traffic Surveillance , 1998, Artif. Intell..

[83]  Patrick Pérez,et al.  Sequential Monte Carlo methods for multiple target tracking and data fusion , 2002, IEEE Trans. Signal Process..

[84]  Tom Heskes,et al.  Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy , 2002, NIPS.

[85]  Steffen L. Lauritzen,et al.  Graphical models in R , 1996 .

[86]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[87]  A. M. Tekalp,et al.  Multiple camera tracking of interacting and occluded human motion , 2001, Proc. IEEE.

[88]  Andrew Blake,et al.  Probabilistic tracking in a metric space , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[89]  D. Chandler,et al.  Introduction To Modern Statistical Mechanics , 1987 .

[90]  Ben Kröse,et al.  Approximate Learning and Inference for Tracking with Non-overlapping Cameras , 2003 .

[91]  Federico Pedersini,et al.  Multi-camera parameter tracking , 2001 .

[92]  Songde Ma,et al.  A Novel Probability Model for Background Maintenance and Subtraction , 2002 .

[93]  Gye-Young Kim,et al.  Model-based tracking of moving object , 1997, Pattern Recognit..

[94]  Uri Lerner,et al.  Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms , 2001, UAI.

[95]  Xavier Boyen,et al.  Tractable Inference for Complex Stochastic Processes , 1998, UAI.

[96]  Michael I. Jordan,et al.  Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones , 1999, Machine Learning.

[97]  Ramakant Nevatia,et al.  Tracking multiple humans in complex situations , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[98]  Paolo Giudici,et al.  Improving Markov Chain Monte Carlo Model Search for Data Mining , 2004, Machine Learning.

[99]  Mark Jerrum,et al.  The Markov chain Monte Carlo method: an approach to approximate counting and integration , 1996 .

[100]  Nando de Freitas,et al.  An Introduction to MCMC for Machine Learning , 2004, Machine Learning.

[101]  Nir Friedman,et al.  The Bayesian Structural EM Algorithm , 1998, UAI.

[102]  Takeo Kanade,et al.  Algorithms for cooperative multisensor surveillance , 2001, Proc. IEEE.

[103]  C. Antoniak Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems , 1974 .

[104]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[105]  Ben J. A. Kröse,et al.  A sequential Bayesian algorithm for surveillance with nonoverlapping cameras , 2005, Int. J. Pattern Recognit. Artif. Intell..

[106]  Andrew Blake,et al.  An HMM-Based Segmentation Method for Traffic Monitoring Movies , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[107]  David Barber,et al.  Bayesian Classification With Gaussian Processes , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[108]  G. Armstrong,et al.  The Maximum Surveillance Society: The Rise of CCTV , 1999 .

[109]  Michael I. Jordan,et al.  Loopy Belief Propagation for Approximate Inference: An Empirical Study , 1999, UAI.