[1] X. Zhu, X. Wu, A. Elmagarmid, Z. Feng, L. Wu, Video data mining: semantic
indexing and event detection from the association perspective, IEEE Trans.
Knowl. Data Eng. 17 (2005) 665–677.
[2] W. Hu, N. Xie, L. Li, X. Zeng, S. Maybank, A survey on visual content-based video
indexing and retrieval. Part C: applications and reviews, IEEE Trans. Syst. Man
Cybern. 41 (2011) 797–819.
[3] T. Quack, V. Ferrari, L. Van Gool, Video mining with frequent itemset configurations,
in: H. Sundaram, M.R. Naphade, J.R. Smith, Y. Rui (Eds.), International
Conference on Image and Video Retrieval (CIVR’06), Lecture Notes in Computer
Science (LNCS), vol. 4071, Springer-Verlag, Tempe, États-Unis, 2006, pp.
360–369, http://dx.doi.org/10.1007/11788034 37.
[4] A. Gaidon, Z. Harchaoui, C. Schmid, Temporal localization of actions with
actoms, IEEE Trans. Pattern Anal. Mach. Intell. 35 (11) (2013) 2782–2795,
http://dx.doi.org/10.1109/TPAMI.2013.65, ISSN: 0162-8828.
[5] R. Poppe, A survey on vision-based human action recognition, Image Vis. Comput.
28 (2010) 976–990.
[6] S.-C. Chen, M.-L. Shyu, C. Zhang, J. Strickrott, Multimedia data mining for traf-
fic video sequences, in: Proceedings of the Second International Workshop on
Multimedia Data Mining, MDM/KDD’2001, San Francisco, CA, USA, August 26,
2001, 2001, pp. 78–86.
[7] R. Emonet, J. Varadarajan, J. Odobez, Extracting and locating temporal motifs
in video scenes using a hierarchical non parametric Bayesian model, in: 2011
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, pp.
3233–3240, http://dx.doi.org/10.1109/CVPR.2011.5995572.
[8] X. Xu, J. Tang, X. Liu, X. Zhang, Human behavior understanding for
video surveillance: recent advance, in: 2010 IEEE International Conference
on Systems Man and Cybernetics (SMC), 2010, pp. 3867–3873,
http://dx.doi.org/10.1109/ICSMC.2010.5641773.
[9] L. Patino, F. Bremond, M. Thonnat, Online learning of activities from
video, in: 2012 IEEE Ninth International Conference on Advanced
Video and Signal-Based Surveillance (AVSS), 2012, pp. 234–239,
http://dx.doi.org/10.1109/AVSS.2012.50.
[10] F. Bashir, A. Khokhar, D. Schonfeld, Object trajectory-based activity classification
and recognition using hidden Markov models, IEEE Trans. Image Process.
16 (2007) 1912–1919.
[11] F. Lv, X. Song, B. Wu, V. Singh, R. Nevatia, Left luggage detection using Bayesian
inference, in: Proceedings of the 9th IEEE International Workshop, 2006.
[12] V. Vu, F. Bremond, M. Thonnat, Human behaviour visualisation and simulation
for automatic video understanding, in: Proc. of the 10th Int. Conf. in Central
Europe on Computer Graphics, Visualization and Computer Vision, Czech
Republic, 2006, pp. 485–492.
[13] S.-W. Lee, K. Mase, Activity and location recognition using wearable sensors,
IEEE Pervasive Comput. 1 (2002) 24–32.
[14] D. Damen, D. Hogg, Explaining activities as consistent groups of events, Int. J.
Comput. Vis. 98 (2012) 83–102.
[15] M. Sridhar, A.G. Cohn, D.C. Hogg, Relational graph mining for learning events
from video, in: Proceedings of the 2010 Conference on STAIRS: Proceedings
of the Fifth Starting AI Researchers’ Symposium, IOS Press, Amsterdam, The
Netherlands, 2010, pp. 315–327.
[16] K.S.R. Dubba, A.G. Cohn, D.C. Hogg, Event model learning from complex videos
using ILP, in: Proceeding of ECAI 2010, the 19th European Conference on Arti-
ficial Intelligence, 2010, pp. 93–98.
[17] D. Moore, I. Essa, Recognizing multitasked activities from video using stochastic
context-free grammar, in: Eighteenth National Conference on Artificial Intelligence,
American Association for Artificial Intelligence, Menlo Park, CA, USA,
2002, pp. 770–776.
[18] R. Romdhane, F. Bremond, B. Boulay, M. Thonnat, Probabilistic Recognition of
Complex Event, in: ICVS 2011, Sophia Antipolis, France, 2011.
[19] E. Jouneau, C. Carincotte, Particle-based tracking model for automatic anomaly
detection, in: 2011 18th IEEE International Conference on Image Processing
(ICIP), 2011, pp. 513–516, http://dx.doi.org/10.1109/ICIP.2011.6116394.
L. Patino, J. Ferryman / Applied Soft Computing 25 (2014) 485–495 495
[20] R. Voorhies, L. Elazary, L. Itti, Neuromorphic Bayesian surprise for farrange
event detection, in: 2012 IEEE Ninth International Conference on
Advanced Video and Signal-Based Surveillance (AVSS), 2012, pp. 1–6,
http://dx.doi.org/10.1109/AVSS.2012.49.
[21] T. Xiang, S. Gong, Video behaviour profiling and abnormality detection
without manual labelling, in: Tenth IEEE International Conference
on Computer Vision, 2005 (ICCV 2005), vol. 2, 2005, pp. 1238–1245,
http://dx.doi.org/10.1109/ICCV.2005.248.
[22] R.R. Sillito, R.B. Fisher, Semi-supervised learning for anomalous trajectory
detection, in: BMVC, 2008, pp. 1–10.
[23] C. Li, Z. Han, Q. Ye, J. Jiao, Visual abnormal behavior detection based on trajectory
sparse reconstruction analysis, Neurocomputing 119 (2013) 94–100
(Intelligent Processing Techniques for Semantic-based Image and Video
Retrieval).
[24] A. Sodemann, M. Ross, B. Borghetti, A review of anomaly detection in automated
surveillance, IEEE Trans. Syst. Man Cybernet. C: Appl. Rev. 42 (2012)
1257–1272.
[25] M. Khan, L. Zhang, Y. Gotoh, Human focused video description, in: 2011 IEEE
International Conference on Computer Vision Workshops (ICCV Workshops),
2011, pp. 1480–1487, http://dx.doi.org/10.1109/ICCVW.2011.6130425.
[26] C. Liu, C. Hu, Q. Liu, J. Aggarwal, Video event description in scene context,
Neurocomputing 119 (2013) 82–93 (Intelligent Processing Techniques for
Semantic-based Image and Video Retrieval).
[27] M. Arens, R. Gerber, H.-H. Nagel, Conceptual representations between video
signals and natural language descriptions, Image Vis. Comput. 26 (2008)
53–66.
[28] M. Popa, L. Rothkrantz, C. Shan, T. Gritti, P. Wiggers, Semantic assessment
of shopping behavior using trajectories shopping related actions and context
information, Pattern Recogn. Lett. 34 (2013) 809–819 (Scene Understanding
and Behaviour Analysis).
[29] S. Mallat, A theory for multiresolution signal decomposition: the
wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell. 11 (1989)
674–693.
[30] J.A. Hartigan, Clustering Algorithms, John Wiley & Sons, Inc., New York, 1975.
[31] R. Duda, P. Hart, D. Stork, Pattern Classification and Scene Analysis, John Wiley
& Sons, Inc., New York, 1995.
[32] B. Rosner, Percentage points for a generalized ESD many-outlier procedure,
Technometrics 25 (1983) 165–172.
[33] E.M. Knorr, R.T. Ng, in: D. Heckerman, H. Mannila, D. Pregibon (Eds.), A Unified
Notion of Outliers: Properties and Computation, AAAI Press, 1997, pp. 219–222.
[34] M.M. Breunig, H.-P. Kriegel, R.T. Ng, J. Sander, LOF: identifying density-based
local outliers, SIGMOD Rec. 29 (2000) 93–104.
[35] S. Ramaswamy, R. Rastogi, K. Shim, Efficient algorithms for mining outliers
from large data sets, in: Proceedings of the 2000 ACM SIGMOD International
Conference on Management of Data, SIGMOD’00, ACM, New York, NY, USA,
2000, pp. 427–438, http://dx.doi.org/10.1145/342009.335437.
[36] E.M. Knorr, R.T. Ng, Algorithms for mining distance-based outliers in large
datasets, in: Proceedings of the 24th International Conference on Very Large
Data Bases, VLDB’98, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA,
1998, pp. 392–403.
[37] Z. He, X. Xu, S. Deng, Discovering cluster-based local outliers, Pattern Recogn.
Lett. 24 (2003) 1641–1650.
[38] H. Fan, Z.O.R.F. Andrew, J. Wu, A nonparametric outlier detection for effectively
discovering top-n outliers from engineering data, Lect. Notes Comput. Sci. 3918
(2006) 557–566.
[39] ARENA (Architecture for the Recognition of thrEats to mobile assets
using Networks of multiple Affordable sensors), FP7 European Project,
https://www.informationsystems.foi.se/∼arena-fp7
[40] CAVIAR (Context Aware Vision using Image-based Active Recognition),
Information Society Technology’s Programme Project, http://homepages.
inf.ed.ac.uk/rbf/CAVIAR/
[41] MIT. Trajectory Dataset, http://www.ee.cuhk.edu.hk/∼xgwang/
MITtrajsinglemulti.html
[42] X. Wang, K. Tieu, W. Grimson, Correspondence-free activity analysis and scene
modeling in multiple camera views, IEEE Trans. Pattern Anal. Mach. Intell. 32
(2010) 56–71.
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