Abnormal trajectory detection method based on enhanced density clustering and abnormal information mining

Ming HE; Gong-da QIU; Bo ZHOU; Qiang LIU; Yu-ting CAO

doi:10.11959/j.issn.1000-436x.2017287

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Abnormal trajectory detection method based on enhanced density clustering and abnormal information mining

Papers | 更新时间：2024-06-05

- Abnormal trajectory detection method based on enhanced density clustering and abnormal information mining
- Journal on Communications Vol. 38, Issue 12, Pages: 21-33(2017)
- 作者机构：
  
  1. 陆军工程大学指挥控制工程学院，江苏南京 210007
  2. 解放军第61所，北京 100000
  3. 海军指挥学院，江苏南京210007
- 作者简介：
- 基金信息：
  
  The Natural Science Foundation of Jiangsu Province(BK20150721);The Natural Science Foundation of Jiangsu Province(BK20161469);China Postdoctoral Science Foundation(2015M582786);China Postdoctoral Science Foundation(2016T91017);The Primary Research ＆ Development Plan of Jiangsu Province(BE2015728);The Primary Research ＆ Development Plan of Jiangsu Province(BE2016904);Engineering Research Center of Jiangsu Province(BM2014391);The National Key Research and Development Program of China(2016YFC0800606)
- DOI：10.11959/j.issn.1000-436x.2017287
  CLC： TP301
- Online First：2017-12，
  
  Published：25 December 2017
- 稿件说明：
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Ming HE, Gong-da QIU, Bo ZHOU, et al. Abnormal trajectory detection method based on enhanced density clustering and abnormal information mining[J]. Journal on Communications, 2017, 38(12): 21-33.
DOI：

Ming HE, Gong-da QIU, Bo ZHOU, et al. Abnormal trajectory detection method based on enhanced density clustering and abnormal information mining[J]. Journal on Communications, 2017, 38(12): 21-33. DOI： 10.11959/j.issn.1000-436x.2017287.

摘要

针对社会安全事件中异常行为信息识别挖掘难等问题，提出一种基于改进密度聚类与模式信息挖掘的异常轨迹识别方法。首先，针对采样问题，结合Hausdorff距离思想重新定义一种改进型DTW距离，用于描述轨迹具体行为，而MBR距离下的延伸定义，则用于描述轨迹覆盖区域热度。其次，在CFSFDP算法的密度关联与决策模型下，基于支持向量机回归（SVR，support vector regression）提出了特定支持向量机回归（SSVR，specific support vector regression），利用针对性改良下的回归差异非线性识别类中心，实现类的智能识别。最后，通过2种密度下的类识别，实现更多异常模式信息的挖掘与3种异常轨迹识别。结合上海市与北京市出租车轨迹集进行了仿真实验与数据分析，验证了算法在轨迹聚类异常识别方面的有效性。与传统方法相比，类发现能力提高了10%，异常轨迹信息得以区别与丰富。

Abstract

Aiming at problems of low accuracy in the recognition and difficulty in enriching the information of abnormal behavior in the social security incidents，an abnormal trajectory detection method based on enhanced density clustering and abnormal information mining was proposed.Firstly，combined with Hausdorff distance，an enhanced DTW distance aiming at the problem of sampling to describe the behavior in detail was proposed.And based on the MBR distance， some definitions to describe the geographical distribution of trajectory were proposed.Secondly，with the density-distance decision model of CFSFDP algorithm，intelligent recognition of cluster was realized by using the difference of SSVR which was proposed based on SVR.Finally，based on the analysis of distribution under the two kinds of density，more abnormal information could be mined，three kinds of abnormal trajectories would be recognized.And the simulation results on trajectory data of Shanghai and Beijing verify that the algorithm is objective and efficient.Comparing to existing method，accuracy in the clustering is promoted by 10%，and the abnormal trajectories are sorted， abnormal information is enriched.

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references

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