【摘要】：進入二十一世紀以來，我國社會和經(jīng)濟得到快速發(fā)展，城市化進程不斷加快，城市道路的交通壓力也在不斷增大，城市交通擁堵現(xiàn)象愈發(fā)嚴重，且機動車數(shù)量的日益增加導致城市道路交通事故頻發(fā)。傳統(tǒng)的交通事故檢測和查閱通常是通過人工監(jiān)測的方式進行的，但這種方法效率低、實時性較差。隨著智能交通的迅速發(fā)展以及計算機視覺技術的廣泛應用，利用視頻圖像處理技術對道路監(jiān)控視頻進行實時分析、智能檢測交通事故、獲取交通信息成為了研究的熱點。對交通事故的實時檢測不僅可以減少警力資源的浪費，而且對于提高交通事故處理效率有著重大的意義。 本文主要基于視頻對交通事故的自動判別算法進行研究，論文的主要研究內容如下：首先介紹了運動目標檢測的技術難點，歸納了初始化背景模型的常用方法，并利用均值法背景建模方法提取背景圖像，獲得初始背景之后，通過背景更新算法使背景及時更新到當前狀態(tài)；然后利用背景差分法提取差分圖像，并對車輛陰影進行去除后進行連通區(qū)域標定，實現(xiàn)運動目標的檢測。通過進行實驗并進行效果分析，驗證了算法的有效性。運動目標跟蹤算法部分，首先介紹了卡爾曼濾波器的工作原理，應用卡爾曼濾波對運動目標進行跟蹤與匹配，利用運動前景的質心距離和面積大小作為匹配參數(shù)；運動目標特征提取部分，首先對攝像機標定算法進行分析，然后根據(jù)前一章運動目標檢測和匹配跟蹤的結果，通過提取前景目標的運動信息，計算目標的速度、行駛方向和軌跡等特征。通過卡爾曼濾波預測運動目標下一幀質心點坐標，并與檢測到的前景質心位置進行比較，，判斷檢測到的前景是否為重合的前景，如果兩個前景目標發(fā)生重合，則發(fā)生了交通沖突。然后，對交通沖突作進一步判斷，提出了綜合車輛減速度、行駛方向變化率和時間參數(shù)的自動判別交通事故的算法，對車輛造成的遮擋情況和偽碰撞現(xiàn)象進行識別，最終判斷交通事故是否發(fā)生。最后通過實驗進行驗證，證明了判別算法的有效性，同時分析了實驗存在的誤差。
[Abstract]:Since the 21 century, the society and economy of our country have been developed rapidly, the process of urbanization has been quickened, the traffic pressure of urban roads is also increasing, and the phenomenon of urban traffic congestion is becoming more and more serious. And the increasing number of motor vehicles leads to frequent urban road traffic accidents. The traditional method of traffic accident detection and inspection is usually carried out by manual monitoring, but this method is inefficient and poor in real time. With the rapid development of intelligent transportation and the wide application of computer vision technology, real-time analysis of road surveillance video using video image processing technology, intelligent detection of traffic accidents, access to traffic information has become a research hotspot. The real-time detection of traffic accidents can not only reduce the waste of police resources, but also improve the efficiency of traffic accidents. The main contents of this paper are as follows: firstly, the technical difficulties of moving target detection are introduced, and the common methods of initializing background model are summarized. The background image is extracted by the mean method, and the background is updated to the current state by the background updating algorithm. Then the background difference method is used to extract the differential image, and then the connected region is calibrated after removing the shadow of the vehicle, and the moving object detection is realized. The effectiveness of the algorithm is verified by experiment and effect analysis. In the part of moving target tracking algorithm, firstly, the working principle of Kalman filter is introduced. Kalman filter is used to track and match moving target, and the centroid distance and area of moving foreground are used as matching parameters. In the part of feature extraction of moving targets, the camera calibration algorithm is analyzed first, and then according to the results of moving target detection and matching and tracking in the previous chapter, the velocity of the target is calculated by extracting the moving information of the foreground target. Driving direction and trajectory and other characteristics. The coordinates of the next frame centroid of moving target are predicted by Kalman filter, and compared with the detected centroid position of foreground, to judge whether the detected foreground is the same prospect, if the two foreground targets overlap, Traffic conflicts occur. Then, the traffic conflict is judged further, and an algorithm is proposed to automatically distinguish traffic accidents by synthesizing vehicle deceleration, direction change rate and time parameters, and to identify the occlusion and pseudo-collision caused by vehicles. Finally determine whether the traffic accident occurred. Finally, the validity of the discriminant algorithm is verified by experiments, and the error of the experiment is analyzed.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491.31;TP391.41

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