16.7.3.3.1 Detecting Anomalies, Trajectory Analysis for Anomalies

Chapter Contents (Back)
Anomaly Detection. Abnormal Event. Trajectory Analysis. General tracking: See also Target Tracking Techniques, Prediction, Trajectory Based. Events: See also Trajectory Analysis for Events, Actions.

Piciarelli, C.[Claudio], Foresti, G.L.[Gian Luca],
On-line trajectory clustering for anomalous events detection,
PRL(27), No. 15, November 2006, pp. 1835-1842.
Elsevier DOI 0609
trajectory clustering; On-line clustering; Behaviour analysis BibRef

Piciarelli, C.[Claudio], Micheloni, C.[Christian], Foresti, G.L.[Gian Luca],
Trajectory-Based Anomalous Event Detection,
CirSysVideo(18), No. 11, November 2008, pp. 1544-1554.
IEEE DOI 0811
BibRef
And:
Anomalous trajectory patterns detection,
ICPR08(1-4).
IEEE DOI 0812
BibRef
And:
Support vector machines for robust trajectory clustering,
ICIP08(2540-2543).
IEEE DOI 0810
BibRef
And:
Kernel-based unsupervised trajectory clusters discovery,
VS08(xx-yy). 0810
BibRef
Earlier:
An Autonomous Surveillance Vehicle for People Tracking,
CIAP05(1140-1147).
Springer DOI 0509
See also Detecting moving people in video streams. BibRef

Piciarelli, C.[Claudio], Foresti, G.L.[Gian Luca],
Surveillance-Oriented Event Detection in Video Streams,
IEEE_Int_Sys(26), No. 3, May-June 2011, pp. 32-41.
IEEE DOI 1107
BibRef
Earlier:
Anomalous trajectory detection using support vector machines,
AVSBS07(153-158).
IEEE DOI 0709
Use Explicit event analysis or Anomaly detection. BibRef

Hu, W.M.[Wei-Ming], Xiao, X.J.[Xue-Juan], Fu, Z.Y.[Zhou-Yu], Xie, D., Tan, T.N.[Tie-Niu], Maybank, S.J.[Steve J.],
A System for Learning Statistical Motion Patterns,
PAMI(28), No. 9, September 2006, pp. 1450-1464.
IEEE DOI 0608
Learn patterns for anomaly detection and prediction of behaviors. Track, then learn patterns of trajectories. Detect anomalies. Some comparisons with others: See also Learning the Distribution of Object Trajectories for Event Recognition. See also Learning Spatio-temporal Patterns for Predicting Object Behaviour. See also Learning Semantic Scene Models From Observing Activity in Visual Surveillance. See also Multi feature path modeling for video surveillance. (these do not use probability distributions on the motion patterns) See also Learning Patterns of Activity Using Real-Time Tracking. See also Application of the Self-Organizing Map to Trajectory Classification. See also Utilizing Learned Motion Patterns to Robustly Track Persons. BibRef

Jiang, F.[Fan], Wu, Y.[Ying], Katsaggelos, A.K.[Aggelos K.],
A Dynamic Hierarchical Clustering Method for Trajectory-Based Unusual Video Event Detection,
IP(18), No. 4, April 2009, pp. 907-913.
IEEE DOI 0903
BibRef
Earlier:
Abnormal Event Detection from Surveillance Video by Dynamic Hierarchical Clustering,
ICIP07(V: 145-148).
IEEE DOI 0709
BibRef

Khalid, S.[Shehzad],
Motion-based behaviour learning, profiling and classification in the presence of anomalies,
PR(43), No. 1, January 2010, pp. 173-186,.
Elsevier DOI 0909
Object trajectory; Dimensionality reduction; Trajectory modelling; Trajectory clustering; Event mining; Anomaly detection; Motion recognition BibRef

Khalid, S.[Shehzad],
Activity classification and anomaly detection using m-mediods based modelling of motion patterns,
PR(43), No. 10, October 2010, pp. 3636-3647.
Elsevier DOI 1007
Object trajectory; Dimensionality reduction; Trajectory modelling; Event mining; Anomaly detection; Motion recognition BibRef

Tung, F.[Frederick], Zelek, J.S.[John S.], Clausi, D.A.[David A.],
Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance,
IVC(29), No. 4, March 2011, pp. 230-240.
Elsevier DOI 1102
Video surveillance; Behaviour understanding; Trajectory analysis; Anomaly detection BibRef

Wiliem, A.[Arnold], Madasu, V.[Vamsi], Boles, W.[Wageeh], Yarlagadda, P.[Prasad],
A suspicious behaviour detection using a context space model for smart surveillance systems,
CVIU(116), No. 2, February 2012, pp. 194-209.
Elsevier DOI 1201
BibRef
Earlier:
An Update-Describe Approach for Human Action Recognition in Surveillance Video,
DICTA10(270-275).
IEEE DOI 1012
BibRef
Earlier:
A Context-Based Approach for Detecting Suspicious Behaviours,
DICTA09(146-153).
IEEE DOI 0912
BibRef
Earlier:
Detecting Uncommon Trajectories,
DICTA08(398-404).
IEEE DOI 0812
Suspicious behaviour; Context; Surveillance system; Security BibRef

Chen, C., Zhang, D., Castro, P.S., Li, N., Sun, L., Li, S., Wang, Z.,
iBOAT: Isolation-Based Online Anomalous Trajectory Detection,
ITS(14), No. 2, 2013, pp. 806-818.
IEEE DOI Global Positioning System; Roads; Trajectory; Anomalous trajectory detection 1307
BibRef

Yang, W.Q.[Wan-Qi], Gao, Y.[Yang], Cao, L.B.[Long-Bing],
TRASMIL: A local anomaly detection framework based on trajectory segmentation and multi-instance learning,
CVIU(117), No. 10, 2013, pp. 1273-1286.
Elsevier DOI 1309
Local anomaly detection BibRef

Laxhammar, R., Falkman, G.,
Online Learning and Sequential Anomaly Detection in Trajectories,
PAMI(36), No. 6, June 2014, pp. 1158-1173.
IEEE DOI 1406
Algorithm design and analysis BibRef

Kang, K.[Kai], Liu, W.B.[Wei-Bin], Xing, W.W.[Wei-Wei],
Motion Pattern Study and Analysis from Video Monitoring Trajectory,
IEICE(E97-D), No. 6, June 2014, pp. 1574-1582.
WWW Link. 1407
Abnormality detection. BibRef

Wan, Y.[Yiwen], Yang, T.I.[Tze-I], Keathly, D., Buckles, B.,
Dynamic scene modelling and anomaly detection based on trajectory analysis,
IET-ITS(8), No. 6, September 2014, pp. 526-533.
DOI Link 1411
pattern clustering BibRef

Kumar, D.[Dheeraj], Bezdek, J.C.[James C.], Rajasegarar, S.[Sutharshan], Leckie, C.[Christopher], Palaniswami, M.[Marimuthu],
A visual-numeric approach to clustering and anomaly detection for trajectory data,
VC(33), No. 3, March 2017, pp. 265-281.
Springer DOI 1702
BibRef

Cosar, S., Donatiello, G., Bogorny, V., Garate, C., Alvares, L.O., Brémond, F.,
Toward Abnormal Trajectory and Event Detection in Video Surveillance,
CirSysVideo(27), No. 3, March 2017, pp. 683-695.
IEEE DOI 1703
Acceleration BibRef

Shin, H., Turchi, D., He, S., Tsourdos, A.,
Behavior Monitoring Using Learning Techniques and Regular-Expressions-Based Pattern Matching,
ITS(20), No. 4, April 2019, pp. 1289-1302.
IEEE DOI 1904
Monitoring, Pattern matching, Trajectory, Anomaly detection, Target tracking, Dictionaries, Europe, Monitoring, pattern matching, dictionary learning BibRef


Rodrigues, R., Bhargava, N., Velmurugan, R., Chaudhuri, S.,
Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection,
WACV20(2615-2623)
IEEE DOI 2006
Predictive models, Trajectory, Legged locomotion, Computational modeling, Training data, Decoding, Testing BibRef

Morais, R.[Romero], Le, V.[Vuong], Tran, T.[Truyen], Saha, B.[Budhaditya], Mansour, M.[Moussa], Venkatesh, S.[Svetha],
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos,
CVPR19(11988-11996).
IEEE DOI 2002
BibRef

Roy, P., Bilodeau, G.,
Adversarially Learned Abnormal Trajectory Classifier,
CRV19(65-72)
IEEE DOI 1908
Trajectory, Generative adversarial networks, Training, Data models, Generators, Image reconstruction, Generative adversarial networks BibRef

Ma, C.[Cong], Miao, Z.J.[Zhen-Jiang], Li, M.[Min], Song, S.[Shaoyue], Yang, M.H.[Ming-Hsuan],
Detecting Anomalous Trajectories via Recurrent Neural Networks,
ACCV18(IV:370-382).
Springer DOI 1906
BibRef

Varadarajan, J.[Jagannadan], Subramanian, R., Ahuja, N., Moulin, P., Odobez, J.M.[Jean-Marc],
Active Online Anomaly Detection Using Dirichlet Process Mixture Model and Gaussian Process Classification,
WACV17(615-623)
IEEE DOI 1609
Gaussian processes, Junctions, Labeling, Mixture models, Surveillance, Trajectory, Videos BibRef

Maiorano, F., Petrosino, A.,
Granular trajectory based anomaly detection for surveillance,
ICPR16(2066-2072)
IEEE DOI 1705
Real-time systems, Rough sets, Surveillance, Training, Trajectory, Granular Computation, Online Anomaly Detection, Outlier Detection, Rough Sets, Surveillance BibRef

Ghrab, N.B.[Najla Bouarada], Fendri, E.[Emna], Hammami, M.[Mohamed],
Abnormal Events Detection Based on Trajectory Clustering,
CGiV16(301-306)
IEEE DOI 1608
BibRef
And:
Clustering-Based Abnormal Event Detection: Experimental Comparison for Similarity Measures' Efficiency,
ICIAR16(367-374).
Springer DOI 1608
feature extraction BibRef

Xu, H.T.[Hong-Teng], Zhou, Y.[Yang], Lin, W.Y.[Wei-Yao], Zha, H.Y.[Hong-Yuan],
Unsupervised Trajectory Clustering via Adaptive Multi-kernel-Based Shrinkage,
ICCV15(4328-4336)
IEEE DOI 1602
Clustering algorithms BibRef

Iscen, A.[Ahmet], Armagan, A.[Anil], Duygulu, P.[Pinar],
What Is Usual in Unusual Videos? Trajectory Snippet Histograms for Discovering Unusualness,
WebScale14(808-813)
IEEE DOI 1409
event anomaly detection BibRef

Jeong, H.[Hawook], Chang, H.J.[Hyung Jin], Choi, J.Y.[Jin Young],
Modeling of moving object trajectory by spatio-temporal learning for abnormal behavior detection,
AVSBS11(119-123).
IEEE DOI 1111
BibRef

Li, C.[Ce], Han, Z.J.[Zhen-Jun], Ye, Q.X.[Qi-Xiang], Jiao, J.B.[Jian-Bin],
Abnormal Behavior Detection via Sparse Reconstruction Analysis of Trajectory,
ICIG11(807-810).
IEEE DOI 1109
BibRef

Espinosa-Isidrón, D.L.[Dustin L.], García-Reyes, E.B.[Edel B.],
A New Dissimilarity Measure for Trajectories with Applications in Anomaly Detection,
CIARP10(193-201).
Springer DOI 1011
BibRef

Nishio, S.[Shuichi], Okamoto, H.[Hiromi], Babaguchi, N.[Noboru],
Hierarchical Anomality Detection Based on Situation,
ICPR10(1108-1111).
IEEE DOI 1008
Pedestrian trajectories. BibRef

Sillito, R.R.[Rowland R.], Fisher, R.B.[Robert B.],
Parametric Trajectory Representations for Behaviour Classification,
BMVC09(xx-yy).
PDF File. 0909
BibRef
Earlier:
Semi-supervised Learning for Anomalous Trajectory Detection,
BMVC08(xx-yy).
PDF File. 0809
BibRef

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Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Detecting Anomalies, Abnormal Behavior In Crowds .


Last update:Oct 19, 2020 at 15:02:28