本文關(guān)鍵詞： 矢量場 層次聚類 加權(quán) 異常檢測　出處：《昆明理工大學》2017年碩士論文　論文類型：學位論文

【摘要】：軌跡分析指的是對運動目標運行軌跡進行分析,以便獲取運動目標的行為。軌跡異常檢測就是通過對軌跡進行分析檢測出其中出現(xiàn)的目標異常行為、異常事件。軌跡異常檢測可應用于颶風、動物遷徙預測,交通流監(jiān)測等方面。隨著衛(wèi)星定位數(shù)據(jù)、交通監(jiān)控視頻數(shù)據(jù)量的迅速增長,軌跡數(shù)據(jù)量與其包含的時空信息也迅速增長,然而通過人工分析數(shù)據(jù)的方式耗時耗力,且容易出現(xiàn)錯誤。本文利用聚類的方式將時空軌跡數(shù)據(jù)劃分為不同的簇,通過計算聚類中心軌跡與待檢測軌跡之間的距離從而自動判別時空軌跡正常與否,以便有效解決各類時空數(shù)據(jù)分析應用。本文首先簡要分析了從視頻數(shù)據(jù)中獲取運動目標軌跡的幾種常見方法的優(yōu)缺點。其次,提出一種矢量場層次聚類的方法對軌跡數(shù)據(jù)進行聚類,解決矢量場軌跡聚類不能自適應聚類類別數(shù)的問題,并且通過加權(quán)矢量場擬合解決噪聲軌跡點對聚類結(jié)果的干擾,增強了算法的魯棒性。最后,通過計算檢測數(shù)據(jù)矢量場與各聚類中心軌跡矢量場的相似度,判定待測試軌跡正常與否。通過對監(jiān)控視頻數(shù)據(jù)上進行的實驗表明,本文提出的軌跡聚類方法與傳統(tǒng)的軌跡聚類相比具有更高的類別適應性與魯棒性,對異常軌跡檢出率達到90%以上。
[Abstract]:Trajectory analysis refers to the trajectory analysis of the moving target, in order to obtain the target behavior. Anomaly detection is based on the trajectory analysis to detect abnormal behavior, which targets abnormal events. Trajectory trajectory outlier detection can be applied to the hurricane, animal migration prediction, traffic flow monitoring. With the development of satellite positioning data the rapid growth of traffic monitoring, the amount of video data, and contains temporal information track data are also growing rapidly. However, through artificial way of analyzing data is time-consuming, error and easy to use. In this way the clustering of trajectory data into different clusters, by calculating the cluster center trajectory and trajectory to be detected between the distance to automatically determine the spatio-temporal trajectory is normal or not, in order to effectively solve various spatio-temporal data analysis applications. This paper briefly analyzes from the optic frequency According to the advantages and disadvantages of several common methods for moving target. Secondly, put forward a kind of vector field hierarchical clustering method to cluster the trajectory data, solve trajectory clustering is not adaptive clustering number vector field, and by weighted vector field fitting to solve noise track points on the clustering results, robustness the algorithm. Finally, by calculating the detection data of vector field and the cluster center trajectory vector field test trajectory similarity, determined to be normal or not. Based on the video surveillance data. Experimental results show that, compared with the trajectory clustering trajectory clustering method proposed in this paper with the traditional categories of adaptability and greater robustness, the abnormal the trajectory detection rate reached more than 90%.

【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：X924.2;TP391.41

【參考文獻】

相關(guān)期刊論文 前9條

1 王蒙;戴亞平;;基于FAST角點檢測的局部魯棒特征[J];北京理工大學學報;2013年10期

2 潘奇明;周文輝;程詠梅;;運動目標軌跡分類與識別[J];火力與指揮控制;2009年11期

3 曲琳;周凡;陳耀武;;基于Hausdorff距離的視覺監(jiān)控軌跡分類算法[J];吉林大學學報(工學版);2009年06期

4 劉良旭;喬少杰;劉賓;樂嘉錦;唐常杰;;基于R-Tree的高效異常軌跡檢測算法[J];軟件學報;2009年09期

5 孫吉貴;劉杰;趙連宇;;聚類算法研究[J];軟件學報;2008年01期

6 查宇飛;張育;畢篤彥;;基于區(qū)域活動輪廓運動目標跟蹤方法研究[J];中國圖象圖形學報;2006年12期

7 林廣宇;魏朗;;基于數(shù)字圖像技術(shù)的汽車行駛軌跡狀態(tài)識別[J];交通運輸工程學報;2006年03期

8 胡衛(wèi)明,謝丹,譚鐵牛,沈俊;軌跡分布模式學習的層次自組織神經(jīng)網(wǎng)絡方法[J];計算機學報;2003年04期

9 李培華,張?zhí)镂?主動輪廓線模型(蛇模型)綜述[J];軟件學報;2000年06期

，

本文編號：1480818