中文摘要：

基于目标轨迹的异常行为检测算法忽略了轨迹内部信息,容易导致异常检测虚警率偏高。为解决该问题,提出一种基于轨迹分段主题模型的视频异常行为检测方法。首先将目标原始轨迹根据轨迹转角分段,然后采用分段量化的方式提取轨迹片段中包含的行为特征信息,接着通过潜在狄利克雷分配（LDA）主题模型建模发掘目标轨迹之间的时空关系,最后通过学习所构建的模型并结合贝叶斯理论进行行为模式分析和异常行为检测。分别对两个视频场景进行了目标行为模式分析和异常行为检测的仿真实验,检测出了场景内多种异常行为模式。实验结果表明,通过结合轨迹分段与LDA主题模型,该算法能够充分挖掘目标轨迹内部的行为特征信息,识别多种异常行为模式,并且能提高对异常行为检测的准确率。

英文摘要：

Most of the current trajectory-based abnormal behavior detection algorithms do not consider the internal information of the trajectory, which might lead to a high false alarm rate. An abnormal behavior detection method based on trajectory segment using the topic model was presented. Firstly, the original trajectories were partitioned into trajectory segments according to turning angles. Secondly, the behavior characteristic information was extracted by quantifying the observations from these segments into different visual words. Then the time-space relationship among the trajectories was explored by Latent Dirichlet Allocation （LDA） model. Finally, the behavior pattern analysis and the abnormal behavior detection could be implemented by learning the corresponding generative topic model combined with the Bayesian theory. Simulation experiments of behavior pattern analysis and abnormal behavior detection were conducted on two video scenes, and different kinds of abnormal behavior patterns were detected. The experimental results show that, combining with trajectory segmentation, the proposed method can dig the internal behavior characteristic information to identify a variety of abnormal behavior patterns and improve the accuracy of abnormal behavior detection.