基于改进的动态聚类的视频异常事件检测
Video Abnormal Event Detection Based on Improved Dynamic Clustering
DOI: 10.12677/AIRR.2018.72009, PDF,    科研立项经费支持
作者: 刘李启明, 徐向华*, 张灵均：杭州电子科技大学，计算机学院，浙江 杭州；浙江省数据存储传输技术重点实验室，浙江 杭州
关键词: 异常事件检测；监控视频；向量合并；动态聚类；Abnormal Event Detection； Monitoring Video； Vector Merge； Dynamic Clustering
摘要: 异常事件检测是智能监控系统中的重要一环，尤其是对复杂的监控视频场景而言。近年来研究者提出了很多的算法来检测视频中的异常事件，然而它们中的大多数都需要在建模过程中给模型设定一系列的参数，这样不仅调参麻烦，而且在更换检测场景时需要重新设定参数。本文提出了一个基于非参数模型的异常检测算法，通过基于运动趋势的向量合并方法来构造并维护一个向量集合，并运用聚类生成出不同的事件簇，同时提出了一个预检测步骤以此来提高算法在稀疏场景下的检测效果。本文选取了一些已有的检测算法进行了对比实验，最后的实验结果表明，本文提出的检测模型在检测率以及时间性能上均有一定的优势。
Abstract: Abnormal event detection is an important part of intelligent surveillance systems, especially for complex surveillance video scenes. In recent years, many algorithms have been proposed to detect abnormal events. However, most of them need to set a series of parameters in the model during the modeling process, which is not only troublesome in arranging the parameters, but also the parameters need to be reset when changing the detect scene. This paper proposes an abnormal detection algorithm based on non-parametric models, constructs and maintains a vector set based on the motion trend vector merging method, and uses clustering to generate different event clusters, and proposes a pre-detection step to improve the detection effect of the algorithm in sparse scenes. Finally, some existing detection algorithms are selected for comparison experiments. The results show that the model proposed in this paper has certain advantages in detection rate and time performance.
文章引用：刘李启明, 徐向华, 张灵均. 基于改进的动态聚类的视频异常事件检测[J]. 人工智能与机器人研究, 2018, 7(2): 78-88. https://doi.org/10.12677/AIRR.2018.72009

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