Journal of University of Chinese Academy of Sciences >
MUGG-based modeling of trajectories and anomaly detection
Received date: 2012-01-19
Revised date: 2012-03-08
Online published: 2012-03-08
A probabilistic model named MUGG (mixture of unilateral generalized Gaussians) is designed for modeling the distribution of trajectories in visual scene. Information of trajectory is calculated to determine whether the trajectory is abnormal. This method is unsupervised and independent of prior knowledge.It is fit for time-varying environment with the real-time updated model. Its availability and robustness shown by experiments proves the application value.
Key words： anomaly detection; MUGG; trajectory learning; trajectory distance
GUI Shu , GUO Li , LU Hai-Xian , XIE Jin-Sheng . MUGG-based modeling of trajectories and anomaly detection[J]. Journal of University of Chinese Academy of Sciences, 2013 , 30(2) : 244 -250 . DOI: 10.7523/j.issn.1002-1175.2013.02.016
[1] Remagnino P, Velastin S A, Foresti G L, et al. Novel concepts and challenges for the next generation of video surveillance systems[J]. Machine Vision and Applications, 2007, 18: 135-137.
[2] Morris B T, Trivedi M M. A survey of vision-based trajectory learning and analysis for surveillance[J]. Circuit and Systems for Video Technology, 2008, 18(8): 1114-1127.
[3] Piciarelli C, Micheloni C, Foresti G L. Trajectory-based anomalous event detection[J]. Circuits and Systems for Video Technology, 2008, 18(11): 1544-1554.
[4] Jung C R, Hennemann L, Musse S R. Event detection using trajectory clustering and 4-D histograms[J]. Circuits and Systems for Video Technology, 2008, 18(11): 1565-1575.
[5] Bashir F I, Khokhar A A, Schonfeld D. Object trajectory-based activity classification and recognition using hidden Markov models[J]. Image Processing, 2007, 16(7): 1912-1919.
[6] Nascimento J C, Figueiredo M, Marques J S. Trajectory classification using switched dynamical hidden Markov models[J]. Image Processing, 2010, 19(5): 1338-1348.
[7] Morris B T, Trivedi M M. Trajectory learning for activity understanding: unsupervised, multilevel, and long-term adaptive approach[J]. Pattern Analysis and Machine Intelligence, 2011, 33(11): 2287-2301.
[8] Morris B T, Trivedi M M. Learning trajectory patterns by clustering: experimental studies and comparative evaluation[J]. Computer Vision and Pattern Recogniton, 2009: 312-319.
[9] Vlachos M, Kollios G, Gunopulos D. Discovering similar multidimensional trajectories[J]. Proc IEEE Conf Data Eng, 2002: 673-684.
[10] Karypis G, Han E H, Kumar V. Chameleon: a hierarchical clustering algorithm using dynamic modeling[J]. IEEE Computer, 1999, 32(8): 68-75.
[11] Karypis G, Kumar V. Multilevel k-way partitioning scheme for irregular graphs[J]. Journal of Parallel and Distributed Computing, 1998, 48(1): 96-129.
/
| 〈 |
|
〉 |