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.W.[Yi-Wen],
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
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 .