Anomaly Detection in Trajectory Data for Surveillance Applications

Abnormal behaviour may indicate important objects and events in a wide variety of domains. One such domain is intelligence and surveillance, where there is a clear trend towards more and more advan ...

[1]  Margaret H. Dunham,et al.  A Comparative Study of Outlier Detection Algorithms , 2009, MLDM.

[2]  F. Johansson,et al.  Detection of vessel anomalies - a Bayesian network approach , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[3]  Allen M. Waxman,et al.  Associative Learning of Vessel Motion Patterns for Maritime Situation Awareness , 2006, 2006 9th International Conference on Information Fusion.

[4]  Bhavani M. Thuraisingham,et al.  Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams , 2009, ECML/PKDD.

[5]  Anders Holst,et al.  Abnormality Detection in Event Data and Condition Counters on Regina Trains , 2006 .

[6]  Harry Wechsler,et al.  A Martingale Framework for Detecting Changes in Data Streams by Testing Exchangeability , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  A. Madansky.  Identification of Outliers , 1988 .

[8]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[9]  Eamonn J. Keogh,et al.  HOT SAX: efficiently finding the most unusual time series subsequence , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[10]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Jung-Min Park,et al.  An overview of anomaly detection techniques: Existing solutions and latest technological trends , 2007, Comput. Networks.

[12]  Rikard Laxhammar,et al.  Anomaly detection for sea surveillance , 2008, 2008 11th International Conference on Information Fusion.

[13]  Sergio A. Velastin,et al.  How close are we to solving the problem of automated visual surveillance? , 2008, Machine Vision and Applications.

[14]  S.J. Chang,et al.  Development and analysis of AIS applications as an efficient tool for vessel traffic service , 2004, Oceans '04 MTS/IEEE Techno-Ocean '04 (IEEE Cat. No.04CH37600).

[15]  Robert B. Fisher,et al.  Semi-supervised Learning for Anomalous Trajectory Detection , 2008, BMVC.

[16]  Bradley J. Rhodes,et al.  Adaptive Mixture-Based Neural Network Approach for Higher-Level Fusion and Automated Behavior Monitoring , 2009, 2009 IEEE International Conference on Communications.

[17]  W. Gasarch,et al.  The Book Review Column 1 Coverage Untyped Systems Simple Types Recursive Types Higher-order Systems General Impression 3 Organization, and Contents of the Book , 2022 .

[18]  Rikard Laxhammar,et al.  Conformal prediction for distribution-independent anomaly detection in streaming vessel data , 2010, StreamKDD '10.

[19]  James B. Kraiman,et al.  Automated anomaly detection processor , 2002, SPIE Defense + Commercial Sensing.

[20]  John K Kruschke,et al.  Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[21]  Tieniu Tan,et al.  Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[22]  Gian Luca Foresti,et al.  On-line trajectory clustering for anomalous events detection , 2006, Pattern Recognit. Lett..

[23]  Jakob J. Verbeek,et al.  Mixture models for clustering and dimension reduction , 2004 .

[24]  Li Wei,et al.  Compression-based data mining of sequential data , 2007, Data Mining and Knowledge Discovery.

[25]  Jean Roy,et al.  Anomaly detection in the maritime domain , 2008, SPIE Defense + Commercial Sensing.

[26]  W. R. Buckland,et al.  Outliers in Statistical Data , 1979 .

[27]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[28]  William Rucklidge,et al.  Efficient Visual Recognition Using the Hausdorff Distance , 1996, Lecture Notes in Computer Science.

[29]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[30]  Jörgen Hansson,et al.  Planning and Implementing your Final Year Project - with Success!: A Guide for Students in Computer Science and Information Systems , 2002 .

[31]  Per M. Gustavsson,et al.  Signature-based activity detection based on Bayesian networks acquired from expert knowledge , 2009, 2009 12th International Conference on Information Fusion.

[32]  Mohan M. Trivedi,et al.  Learning and Classification of Trajectories in Dynamic Scenes: A General Framework for Live Video Analysis , 2008, 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance.

[33]  Sameer Singh,et al.  Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..

[34]  Andrew Hunter,et al.  Application of the self-organising map to trajectory classification , 2000, Proceedings Third IEEE International Workshop on Visual Surveillance.

[35]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[36]  Salvatore J. Stolfo,et al.  A Geometric Framework for Unsupervised Anomaly Detection , 2002, Applications of Data Mining in Computer Security.

[37]  Tieniu Tan,et al.  A system for learning statistical motion patterns , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[39]  Lars Niklasson,et al.  An ensemble approach for increased anomaly detection performance in video surveillance data , 2009, 2009 12th International Conference on Information Fusion.

[40]  Vladimir Vovk,et al.  A tutorial on conformal prediction , 2007, J. Mach. Learn. Res..

[41]  Mohan M. Trivedi,et al.  A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[42]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[43]  Michal Pechoucek,et al.  Probabilistic Modeling of Mobile Agents' Trajectories , 2010, ADMI.

[44]  Bradley J. Rhodes,et al.  Anomaly Detection & Behavior Prediction: Higher-Level Fusion Based on Computational Neuroscientific Principles , 2009 .

[45]  Bernhard Schölkopf,et al.  Support Vector Method for Novelty Detection , 1999, NIPS.

[46]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[47]  Helmut Alt,et al.  Approximate matching of polygonal shapes , 1995, SCG '91.

[48]  Ben J. A. Kröse,et al.  Efficient Greedy Learning of Gaussian Mixture Models , 2003, Neural Computation.

[49]  Lars Niklasson,et al.  Evaluating precise and imprecise State-Based Anomaly detectors for maritime surveillance , 2010, 2010 13th International Conference on Information Fusion.

[50]  Shehzad Khalid,et al.  Classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space , 2006, Multimedia Systems.

[51]  F. Porikli.  Trajectory Distance Metric Using Hidden Markov Model Based Representation , 2004 .

[52]  Lars Niklasson,et al.  Situation Recognition and Hypothesis Management Using Petri Nets , 2009, MDAI.

[53]  Mark R. Morelande,et al.  Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction , 2008, 2008 11th International Conference on Information Fusion.

[54]  Tieniu Tan,et al.  Similarity based vehicle trajectory clustering and anomaly detection , 2005, IEEE International Conference on Image Processing 2005.

[55]  Jae-Gil Lee,et al.  Trajectory Outlier Detection: A Partition-and-Detect Framework , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[56]  Sameer Singh,et al.  Novelty detection: a review - part 1: statistical approaches , 2003, Signal Process..

[57]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[58]  Lars Niklasson,et al.  Trajectory clustering for coastal surveillance , 2007, 2007 10th International Conference on Information Fusion.

[59]  Venkatesh Saligrama,et al.  Anomaly Detection with Score functions based on Nearest Neighbor Graphs , 2009, NIPS.

[60]  David J. Hand,et al.  Classifier Technology and the Illusion of Progress , 2006, math/0606441.

[61]  Bradley J. Rhodes,et al.  Probabilistic associative learning of vessel motion patterns at multiple spatial scales for maritime situation awareness , 2007, 2007 10th International Conference on Information Fusion.

[62]  Clara Pizzuti,et al.  Fast Outlier Detection in High Dimensional Spaces , 2002, PKDD.

[63]  Helmut Alt,et al.  The Computational Geometry of Comparing Shapes , 2009, Efficient Algorithms.

[64]  Maria Riveiro,et al.  Visual analytics for maritime anomaly detection , 2011 .

[65]  Alexander Gammerman,et al.  Hedging predictions in machine learning , 2006, ArXiv.

[66]  Stefan Axelsson,et al.  The base-rate fallacy and the difficulty of intrusion detection , 2000, TSEC.

[67]  Leonid Portnoy,et al.  Intrusion detection with unlabeled data using clustering , 2000 .

[68]  Eamonn J. Keogh,et al.  Disk aware discord discovery: finding unusual time series in terabyte sized datasets , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[69]  Egils Sviestins,et al.  Rule-based situation assessment for sea surveillance , 2006, SPIE Defense + Commercial Sensing.

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

[71]  Göran Falkman,et al.  Anomaly detection in sea traffic - A comparison of the Gaussian Mixture Model and the Kernel Density Estimator , 2009, 2009 12th International Conference on Information Fusion.

[72]  Dawei Liu,et al.  Efficient anomaly monitoring over moving object trajectory streams , 2009, KDD.

[73]  Gian Luca Foresti,et al.  Trajectory-Based Anomalous Event Detection , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[74]  Anders Holst,et al.  Incremental stream clustering and anomaly detection , 2008 .

[75]  Aleksandar Lazarevic,et al.  Outlier Detection with Kernel Density Functions , 2007, MLDM.

[76]  Aleksandar Lazarevic,et al.  Incremental Local Outlier Detection for Data Streams , 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

[77]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[78]  Nassim Nicholas Taleb.  Fooled by randomness : the hidden role of chance in the markets and in life , 2001 .

[79]  Lars Niklasson,et al.  Finding behavioural anomalies in public areas using video surveillance data , 2008, 2008 11th International Conference on Information Fusion.

[80]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[81]  Nikolaos Papanikolopoulos,et al.  Clustering of Vehicle Trajectories , 2010, IEEE Transactions on Intelligent Transportation Systems.