17.1.2.3.4 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

Doshi, K.[Keval], Yilmaz, Y.[Yasin],
Online anomaly detection in surveillance videos with asymptotic bound on false alarm rate,
PR(114), 2021, pp. 107865.
Elsevier DOI 2103
Video surveillance, Anomaly detection, Asymptotic performance analysis, Deep learning, Online detection BibRef

Nguyen, D.[Duong], Vadaine, R.[Rodolphe], Hajduch, G.[Guillaume], Garello, R.[René], Fablet, R.[Ronan],
GeoTrackNet: A Maritime Anomaly Detector Using Probabilistic Neural Network Representation of AIS Tracks and 'A Contrario' Detection,
ITS(23), No. 6, June 2022, pp. 5655-5667.
IEEE DOI 2206
Artificial intelligence, Anomaly detection, Trajectory, Probabilistic logic, Task analysis, Geospatial analysis, Detectors, a contrario detection BibRef

Yang, J.W.[Jia-Wei], Tan, X.[Xu], Rahardja, S.[Sylwan],
MiPo: How to Detect Trajectory Outliers with Tabular Outlier Detectors,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Watts, J.[Jeremy], van Wyk, F.[Franco], Rezaei, S.[Shahrbanoo], Wang, Y.Y.[Yi-Yang], Masoud, N.[Neda], Khojandi, A.[Anahita],
A Dynamic Deep Reinforcement Learning-Bayesian Framework for Anomaly Detection,
ITS(23), No. 12, December 2022, pp. 22884-22894.
IEEE DOI 2212
Anomaly detection, Heuristic algorithms, Data models, Convolutional neural networks, Vehicle dynamics, partial information BibRef

Singh, S.K.[Sandeep Kumar], Fowdur, J.S.[Jaya Shradha], Gawlikowski, J.[Jakob], Medina, D.[Daniel],
Leveraging Graph and Deep Learning Uncertainties to Detect Anomalous Maritime Trajectories,
ITS(23), No. 12, December 2022, pp. 23488-23502.
IEEE DOI 2212
Trajectory, Uncertainty, Data models, Artificial intelligence, Anomaly detection, Predictive models, Computational modeling, uncertainty BibRef

Zeng, X.L.[Xian-Lin], Jiang, Y.L.[Ya-Long], Ding, W.R.[Wen-Rui], Li, H.G.[Hong-Guang], Hao, Y.F.[Ya-Feng], Qiu, Z.F.[Zi-Feng],
A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos,
CirSysVideo(33), No. 1, January 2023, pp. 200-212.
IEEE DOI 2301
Videos, Skeleton, Pose estimation, Anomaly detection, Feature extraction, Data models, Convolutional neural networks, understanding of scenes BibRef

Lei, C.[Cailin], Zhao, C.[Cong], Ji, Y.X.[Yu-Xiong], Shen, Y.[Yu], Du, Y.C.[Yu-Chuan],
Identifying and correcting the errors of vehicle trajectories from roadside millimetre-wave radars,
IET-ITS(17), No. 2, 2023, pp. 418-434.
DOI Link 2302
BibRef

Chen, C.M.[Chuan-Ming], Xu, D.S.[Dong-Sheng], Yu, Q.Y.[Qing-Ying], Gong, S.[Shan], Shi, G.[Gege], Liu, H.M.[Hao-Ming], Chen, W.[Wen],
Abnormal-Trajectory Detection Method Based on Variable Grid Partitioning,
IJGI(12), No. 2, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Jézéquel, L.[Loďc], Vu, N.S.[Ngoc-Son], Beaudet, J.[Jean], Histace, A.[Aymeric],
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks,
IP(32), 2023, pp. 807-821.
IEEE DOI 2301
Task analysis, Anomaly detection, Training, Feature extraction, Self-supervised learning, Faces, Neural networks, one-class learning BibRef

Wang, Z.Y.[Zhong-Yue], Chen, Y.[Ying],
Anomaly detection with dual-stream memory network,
JVCIR(90), 2023, pp. 103739.
Elsevier DOI 2301
Anomaly detection, Memory network, Optical flow, Memory sharing BibRef

Hu, J.[Jia], Kaur, K.[Kuljeet], Lin, H.[Hui], Wang, X.D.[Xiao-Ding], Hassan, M.M.[Mohammad Mehedi], Razzak, I.[Imran], Hammoudeh, M.[Mohammad],
Intelligent Anomaly Detection of Trajectories for IoT Empowered Maritime Transportation Systems,
ITS(24), No. 2, February 2023, pp. 2382-2391.
IEEE DOI 2302
Trajectory, Marine vehicles, Anomaly detection, Data models, Analytical models, Transportation, Artificial intelligence, transfer learning BibRef

Lu, Y.[Yue], Cao, C.Q.[Cong-Qi], Zhang, Y.F.[Yi-Fan], Zhang, Y.N.[Yan-Ning],
Learnable Locality-Sensitive Hashing for Video Anomaly Detection,
CirSysVideo(33), No. 2, February 2023, pp. 963-976.
IEEE DOI 2302
Testing, Codes, Training, Hash functions, Costs, Anomaly detection, Neural networks, Video anomaly detection, unsupervised, video analysis and understanding BibRef

Wang, X.D.[Xiao-Ding], Liu, W.X.[Wen-Xin], Lin, H.[Hui], Hu, J.[Jia], Kaur, K.[Kuljeet], Hossain, M.S.[M. Shamim],
AI-Empowered Trajectory Anomaly Detection for Intelligent Transportation Systems: A Hierarchical Federated Learning Approach,
ITS(24), No. 4, April 2023, pp. 4631-4640.
IEEE DOI 2304
Trajectory, Anomaly detection, Data models, Roads, Big Data, Machine learning algorithms, Uncertainty, Anomaly detection, blockchain BibRef

Raja, G.[Gunasekaran], Begum, M.[Mubeena], Gurumoorthy, S.[Sugeerthi], Rajendran, D.S.[Deepak Suresh], Srividya, P.[Ponnada], Dev, K.[Kapal], Qureshi, N.M.F.[Nawab Muhammad Faseeh],
AI-Empowered Trajectory Anomaly Detection and Classification in 6G-V2X,
ITS(24), No. 4, April 2023, pp. 4599-4607.
IEEE DOI 2304
Trajectory, Anomaly detection, 6G mobile communication, Measurement, Behavioral sciences, Security, Decision making, 6G-V2X, distance metrics BibRef

Wang, C.N.[Chun-Nan], Liang, C.[Chen], Chen, X.[Xiang], Wang, H.Z.[Hong-Zhi],
Identifying effective trajectory predictions under the guidance of trajectory anomaly detection model,
PR(140), 2023, pp. 109559.
Elsevier DOI 2305
Stochastic trajectory prediction, Anomaly detection, Trajectory anomaly detection, Automated machine learning BibRef

Gao, J.[Jie], Zhong, B.[Bineng], Chen, Y.[Yan],
Robust Tracking via Learning Model Update With Unsupervised Anomaly Detection Philosophy,
CirSysVideo(33), No. 5, May 2023, pp. 2330-2341.
IEEE DOI 2305
Target tracking, Anomaly detection, Reliability, Noise measurement, Visualization, Philosophical considerations, Transformers, template updating BibRef

Mahajan, V.[Vishal], Barmpounakis, E.[Emmanouil], Alam, M.R.[Md. Rakibul], Geroliminis, N.[Nikolas], Antoniou, C.[Constantinos],
Treating Noise and Anomalies in Vehicle Trajectories From an Experiment With a Swarm of Drones,
ITS(24), No. 9, September 2023, pp. 9055-9067.
IEEE DOI 2310
BibRef


Stergiou, A.[Alexandros], de Weerdt, B.[Brent], Deligiannis, N.[Nikos],
Holistic Representation Learning for Multitask Trajectory Anomaly Detection,
WACV24(6715-6725)
IEEE DOI 2404
Representation learning, Visualization, Interpolation, Extrapolation, Tensors, Motion segmentation, Algorithms, Video recognition and understanding BibRef

Xu, X.Y.[Xiang-Yu], Dunn, E.[Enrique],
GTT-Net: Learned Generalized Trajectory Triangulation,
ICCV21(5775-5784)
IEEE DOI 2203
Training, Geometry, Solid modeling, Sequential analysis, Supervised learning, Streaming media, Stereo, Gestures and body pose BibRef

Cai, X.[Xumin], Aydin, B.[Berkay], Ji, A.[Anli], Angryk, R.[Rafal],
A Framework for Local Outlier Detection from Spatio-Temporal Trajectory Datasets,
ICPR21(5682-5689)
IEEE DOI 2105
Data integrity, Predictive models, Feature extraction, Data models, Trajectory, Task analysis, Anomaly detection 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.Y.[Shao-Yue], 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

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

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Detecting Anomalies, Abnormal Behavior In Crowds .


Last update:Jun 17, 2024 at 21:38:11