【摘要】：交通管理系統(tǒng)是對交通系統(tǒng)中包括人員、車輛、道路及環(huán)境等要素的統(tǒng)籌系統(tǒng),交通系統(tǒng)中的要素一旦發(fā)生異常,就會導致?lián)矶禄蚴鹿�。及時獲取交通信息上報交通管理系統(tǒng)處理,能夠降低擁堵或事故的發(fā)生率,交通監(jiān)控系統(tǒng)是交通管理系統(tǒng)對交通要素實時信息獲取的重要手段�；谝曨l的車輛異常行為分析作為智能交通監(jiān)控系統(tǒng)的核心技術,對于提高道路運行效率、降低事故率及保障交通人生命財產(chǎn)安全有有重要的意義和應用價值。本文以軌跡作為車輛行為信息的載體,圍繞車輛異常行為識別這個目的,進行了運動車輛檢測、跟蹤及異常軌跡識別的研究。針對交通監(jiān)控中無法采集大量負類樣本,并且需要實時檢測的特點,本文提出基于自適應單類支持向量機的車輛異常行為檢測方法。該方法將車輛軌跡映射為高維空間中的向量點,選取一段正常車輛軌跡,由支持向量機算法學習該樣本軌跡后建立支持向量模型,實現(xiàn)對待檢測軌跡進行異常監(jiān)測。 本文所做的工作總結(jié)起來主要為以下幾個方面： (1)針對交通場景中實時性及精確性的要求,采取混合高斯模型對監(jiān)控場景進行背景建模提取運動車輛,較好地抑制了噪聲,并且檢測目標精確。 (2)利用MeanShift算法對檢測到的運動車輛實現(xiàn)軌跡跟蹤,為車輛軌跡異常行為識別獲取基礎信息。 (3)利用改進的Hausdorff距離和比較置信度度量軌跡間的帶權相似度,并用譜聚類算法對其進行聚類,獲得場景中車輛運動行為模式。 (4)以車輛軌跡為研究目標,用單類支持向量機對其進行異常識別,并在單類支持向量機中加入自適應參數(shù),實現(xiàn)支持向量模型的實時更新,以滿足長期監(jiān)控的需求。
[Abstract]:Traffic management system (TMS) is an integrated system which includes personnel, vehicles, roads and environment. Once the elements of traffic system are abnormal, it will lead to congestion or accidents. Getting traffic information in time to report to traffic management system for processing can reduce the incidence of congestion or accident. Traffic monitoring system is an important means for traffic management system to obtain real-time information of traffic elements. As the core technology of intelligent traffic monitoring system, video based abnormal behavior analysis of vehicles has important significance and application value in improving the efficiency of road operation, reducing the accident rate and ensuring the safety of people's lives and property. In this paper, the track is used as the carrier of vehicle behavior information, and the research of moving vehicle detection, tracking and abnormal track recognition is carried out around the purpose of vehicle abnormal behavior recognition. In view of the fact that a large number of negative class samples can not be collected in traffic monitoring and need to be detected in real time, an adaptive single-class support vector machine based vehicle anomaly detection method is proposed in this paper. In this method, the vehicle trajectory is mapped to a vector point in the high-dimensional space, a normal vehicle trajectory is selected, and the support vector model is established after learning the sample trajectory by using the support vector machine algorithm, and the abnormal detection trajectory is monitored. The main work of this paper is summarized as follows: (1) aiming at the requirement of real-time and accuracy in traffic scene, the mixed Gao Si model is used to model the background of the monitoring scene to extract the moving vehicle. The noise is suppressed and the target is detected accurately. (2) MeanShift algorithm is used to track the track of the detected moving vehicle, and the basic information is obtained for the identification of the abnormal behavior of the vehicle trajectory. (3) using the improved Hausdorff distance and the comparative confidence degree to measure the weighted similarity between the trajectories, we use the spectral clustering algorithm to cluster it, and obtain the vehicle motion behavior pattern in the scene. (4) taking the vehicle trajectory as the research object, the single class support vector machine (SVM) is used to identify the anomaly, and the adaptive parameters are added to the single class support vector machine to realize the real-time updating of the support vector model to meet the needs of long-term monitoring.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U491;U495

【參考文獻】

相關期刊論文 前4條

1 陳繼東;孟小峰;賴彩鳳;;基于道路網(wǎng)絡的對象聚類[J];軟件學報;2007年02期

2 王孝艷;張艷珠;董慧穎;李媛;李小娟;;運動目標檢測的三幀差法算法研究[J];沈陽理工大學學報;2011年06期

3 袁冠;夏士雄;張磊;周勇;;基于結(jié)構(gòu)相似度的軌跡聚類算法[J];通信學報;2011年09期

4 李英姿,張飛舟,林耀海;智能交通系統(tǒng)中地理信息系統(tǒng)的研究[J];中國公路學報;2000年03期

，

本文編號：2415762