| 基于MUGG的轨迹建模与异常检测 |
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| 作者姓名： | 桂树 郭立 陆海先 谢锦生 |
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| 作者单位： | 1. 中国科学技术大学信息科学技术学院, 合肥 230022;
2. 电子工程学院, 合肥 230037 |
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| 基金项目： | 国家自然科学基金(61071173)资助 |
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| 摘 要： | 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.
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| 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 |
| 收稿时间： | 2012-01-19 |
| 修稿时间： | 2012-03-08 |
MUGG-based modeling of trajectories and anomaly detection |
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| Authors: | GUI Shu GUO Li LU Hai-Xian XIE Jin-Sheng |
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| Institution: | 1. College of Information Science and Technology, University of Science and Technology of China, Hefei 230022, China;
2. Electronic Engineering Institute, Hefei 230037, China |
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| Abstract: | 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. |
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| Keywords: | anomaly detection MUGG trajectory learning trajectory distance |
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