收稿日期: 2012-01-19
修回日期: 2012-03-08
网络出版日期: 2012-03-08
基金资助
国家自然科学基金(61071173)资助
MUGG-based modeling of trajectories and anomaly detection
Received date: 2012-01-19
Revised date: 2012-03-08
Online published: 2012-03-08
构建视频场景中目标轨迹分布的概率模型——混合单边广义高斯模型,通过计算目标轨迹的信息量分析目标轨迹是否异常.该方法不依赖场景的先验知识,模型建立过程无监督,且模型能实时更新以适应时变环境.实验表明,该方法的有效性和鲁棒性,具有一定的应用价值.
关键词： 异常检测; 混合单边广义高斯模型; 轨迹学习; 轨迹距离
桂树 , 郭立 , 陆海先 , 谢锦生 . 基于MUGG的轨迹建模与异常检测[J]. 中国科学院大学学报, 2013 , 30(2) : 244 -250 . DOI: 10.7523/j.issn.1002-1175.2013.02.016
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
[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.
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