© The Institution of Engineering and Technology
Some urban scenes exhibit periodic variations that can be relevant to visual surveillance applications. One example is the variation in the background elements, such as those caused by moving escalators, lights and scrolling advertisements. When modelled correctly, the incorporation of these periodic elements as a Markov model in a foreground detection component can improve the performance significantly. Another area where the periodicity in the scene can be used is anomaly detection. In some underground metro stations where the flow of people is periodic, deviations from this periodicity can be interpreted as abnormal movements of people. This can be achieved by using a higher-dimensional model for the underlying data structure, and mapping it to a one-dimensional signal for interpretation. This approach is tested, and the results show that abnormal behaviour can be automatically detected.
References
-
-
1)
-
Y. Liao ,
V.R. Vemuri ,
A. Pasos
.
Adaptive anomaly detection with evolving connectionist systems.
J. Netw. Comput. Appl.
,
1 ,
60 -
80
-
2)
-
R.A. Frota ,
G.A. Barreto ,
J.C.M. Mota
.
Anomaly detection in mobile communication networks using the self-organizing map.
J. Intell. Fuzzy Syst.
,
5 ,
493 -
500
-
3)
-
Xiang, T., Gong, S.: `Incremental visual behaviour modelling', Proc. IEEE Visual Surveillance Workshop, May 2006, Graz, p. 65–72.
-
4)
-
S.D.J. Mcarthur ,
C.D. Booth ,
J.R. Mcdonald ,
I.T. Mcfadyen
.
An agent-based anomaly detection architecture for condition monitoring.
IEEE Trans. Power Syst.
,
4 ,
1675 -
1682
-
5)
-
R. Bracewell
.
(1965)
The Fourier transform and its applications.
-
6)
-
Fuglede, B., Topsoe, F.: `Jensen–Shannon divergence and hilbert space embedding', Proc. Int. Symp. Information Theory, 2004, Chicago, p. 31.
-
7)
-
M. Burgess
.
Probabilistic anomaly detection in distributed computer networks.
Sci. Comput. Program.
,
1 ,
1 -
26
-
8)
-
Xiang, T., Gong, S.: `Video behaviour profiling and abnormality detection without manual labelling', Proc. IEEE Int. Conf. Computer Vision (ICCV), 2005, p. 1238–1245.
-
9)
-
Hung, H., Gong, S.: `Detecting and quantifying unusual interactions by correlating salient motion', Proc. IEEE Int. Conf. Advanced Video and Signal based Surveillance, September 2005, Como.
-
10)
-
M. Thottan ,
C. Ji
.
Anomaly detection in IP networks.
IEEE Trans. Signal Process.
,
8 ,
2191 -
2204
-
11)
-
Zelnik-Manor, L., Perona, P.: `Self-tuning spectral clustering', Neural Information Processing Systems (NIPS), 2004, p. 1601–1608.
-
12)
-
Chuah, M., Fu, F.: `ECG anomaly detection via time series analysis', Lecture Notes in Computer Science: Frontiers of High Performance Computing and Networking ISPA 2007 Workshops, 2007, Springer, p. 123–135.
-
13)
-
B. Brophy ,
K. Kelly ,
G. Byrne
.
AI-based condition monitoring of the drilling process.
J. Mater. Process. Technol.
,
3 ,
305 -
310
-
14)
-
van Hateren, J.H., van der Schaaf, A.: `Temporal properties of natural scenes', IS&T/SPIE Proc. (Human Vision and Electronic Imaging), January 1996, 2657, p. 139–143.
-
15)
-
Fawcett, T.: `ROC graphs: notes and practical considerations for researchers', Technical Report HPL-2003–2004, .
-
16)
-
Colombo, A., Leung, V., Orwell, J., Velastin, S.A.: `Markov models of periodically varying backgrounds for change detection', Visual Information Engineering (VIE) 2007, July 2007, London, IET.
-
17)
-
Andrade, E.L., Blunsden, S.J., Fisher, R.B.: `Performance analysis of event detection models in crowded scenes', Visual Information Engineering (VIE) Conf., 2006, Bangalore, India, p. 427–432.
-
18)
-
Ostaszewski, M., Seredynski, F., Bouvry, P.: `A nonself space approach to network anomaly detection', 20thInt. Parallel and Distributed Processing Symp. (IPDPS), NIDISC, April 2006, Greece.
-
19)
-
Maxion, R.A., Tan, K.M.C.: `Benchmarking anomaly-based detection systems', Proc. 1st Int. Conf. Dependable Systems and Networks, June 2000, New York, USA, p. 623–630.
-
20)
-
Wei, L., Kumar, N., Lolla, V., Keogh, E., Lonardi, S., Ratanamahatana, C.A.: `Assumption-free anomaly detection in time series', Proc. 17th Int. Scientific and Statistical Database Management Conf. (SSDBM), June 2005, Santa Barbara, USA, p. 237–242.
-
21)
-
C. Stauffer ,
E. Grimson
.
Learning patterns of activity using real-time tracking.
IEEE Trans. Pattern Recognit. Mach. Intell.
,
8 ,
747 -
757
-
22)
-
Zhong, J., Sclaroff, S.: `Segmenting foreground objects from a dynamic textured background via a robust kalman filter', Proc. ICCV, 2003, p. 44–50.
-
23)
-
Mahoney, M.V., Chan, P.K.: `Trajectory boundary modelling of time series for anomaly detection', Workshop on Data Mining Methods for Anomaly Detection, SIGKDD Conf., 2005, Chicago, IL, USA, p. 32–40.
-
24)
-
Soatto, S., Doretto, G., Wu, Y.: `Dynamic textures', Proc. Int. Conf. Computer Vision, 2001, p. 439–446.
-
25)
-
Oh, S.M., Rehg, J.M., Balch, T., Dellaret, F.: `Data-driven MCMC for learning and inference in switching linear dynamic systems', 20thNational Conf. Artificial Intelligence (AAAI-2005), 2005, Pittsburgh, USA.
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