本文選題：陰影去除 + Kalman濾波算法�。� 參考：《蘭州交通大學(xué)》2017年碩士論文

【摘要】：近年來(lái),隨著計(jì)算機(jī)存儲(chǔ)和運(yùn)算能力的不斷提高,人工智能、模式識(shí)別技術(shù)的迅猛發(fā)展,基于視頻的交通事件檢測(cè)技術(shù)成為智能交通領(lǐng)域研究的熱點(diǎn)問(wèn)題。交通事件自動(dòng)檢測(cè)系統(tǒng)是交通視頻監(jiān)控系統(tǒng)智能化和自動(dòng)化的關(guān)鍵,為快速處理交通事件、減少交通延誤、避免二次交通事故的發(fā)生提供條件,為高速公路運(yùn)營(yíng)管理提供了新的突破口。但如何高效、準(zhǔn)確、快速地實(shí)現(xiàn)交通事件自動(dòng)檢測(cè),仍是當(dāng)前智能交通領(lǐng)域面對(duì)的一大難題。本文從實(shí)際應(yīng)用出發(fā),以高速公路視頻序列為研究對(duì)象,從運(yùn)動(dòng)目標(biāo)檢測(cè)、跟蹤和異常行為描述等幾個(gè)關(guān)鍵技術(shù)著手進(jìn)行研究,設(shè)計(jì)了高速公路逆行、停車、變道異常事件自動(dòng)檢測(cè)算法。本文對(duì)上述三種異常事件的研究主要包括以下幾個(gè)方面的內(nèi)容:在運(yùn)動(dòng)目標(biāo)檢測(cè)方面,采用均值法建立背景模型,以背景差法提取運(yùn)動(dòng)目標(biāo)前景;針對(duì)存在陰影的運(yùn)動(dòng)目標(biāo)前景,提出了一種基于邊緣和HSV顏色空間相結(jié)合的方法去除陰影,并結(jié)合形態(tài)學(xué)處理方法提取出完整的運(yùn)動(dòng)目標(biāo)前景,為有效的運(yùn)動(dòng)目標(biāo)跟蹤提供了基礎(chǔ)。在運(yùn)動(dòng)目標(biāo)跟蹤方面,以車輛的質(zhì)心和面積為基本特征對(duì)車輛進(jìn)行跟蹤,結(jié)合Kalman濾波算法尋求運(yùn)動(dòng)目標(biāo)特征的最優(yōu)估計(jì),利用歐式距離計(jì)算運(yùn)動(dòng)目標(biāo)的位置距離和面積大小差異尋找最佳匹配完成運(yùn)動(dòng)目標(biāo)的跟蹤;針對(duì)車輛間遮擋會(huì)使跟蹤目標(biāo)丟失的現(xiàn)象,本文提出了面積篩選的方法用不同的方式對(duì)車輛進(jìn)行跟蹤,最終獲得車輛的運(yùn)動(dòng)軌跡,為異常事件的判斷提供了依據(jù)。在異常事件檢測(cè)方面,通過(guò)分析車輛的運(yùn)動(dòng)軌跡可以直觀的了解車輛的運(yùn)動(dòng)方向,將車輛的運(yùn)動(dòng)方向與道路規(guī)定的正方向進(jìn)行比較判斷車輛逆行事件;通過(guò)分析車輛的運(yùn)動(dòng)軌跡可以間接獲得車輛的瞬時(shí)速度、加速度、質(zhì)心位置變化等交通參數(shù),分析這些交通參數(shù)的變化判斷車輛是否發(fā)生違章停車事件;通過(guò)分析車輛運(yùn)動(dòng)軌跡與基準(zhǔn)車道線間距離的離散程度判斷車輛是否發(fā)生變道事件。本文對(duì)不同路段高速公路實(shí)際交通視頻序列進(jìn)行測(cè)試,實(shí)驗(yàn)結(jié)果驗(yàn)證了本文異常事件自動(dòng)檢測(cè)算法行之有效,能夠準(zhǔn)確的檢測(cè)出逆行、停車、變道異常事件,具有很好的實(shí)用性。
[Abstract]:In recent years, with the continuous improvement of computer storage and computing ability, artificial intelligence, pattern recognition technology, the rapid development of video-based traffic incident detection technology has become a hot issue in the field of intelligent transportation. The automatic detection system of traffic events is the key to intelligent and automatic traffic video surveillance system. It provides conditions for dealing with traffic incidents quickly, reducing traffic delays and avoiding secondary traffic accidents. It provides a new breakthrough for highway operation and management. However, how to realize the automatic detection of traffic events efficiently, accurately and quickly is still a big problem in the field of intelligent transportation. Based on the practical application, this paper takes the video sequence of freeway as the research object, studies several key technologies such as moving target detection, tracking and abnormal behavior description, and designs the retrograde and parking of expressway. An algorithm for automatic detection of abnormal events with variable traces. In this paper, the research of the above three kinds of abnormal events mainly includes the following aspects: in the aspect of moving target detection, the background model is established by means method, and the foreground of moving target is extracted by background difference method; Aiming at the foreground of moving target with shadow, a method based on edge and HSV color space is proposed to remove shadow, and the whole foreground of moving target is extracted by morphological processing. It provides the foundation for effective moving target tracking. In the aspect of moving target tracking, the center of mass and area of the vehicle are taken as the basic features to track the vehicle, and the Kalman filter algorithm is used to find the optimal estimation of the moving target feature. Using Euclidean distance to calculate the difference of position distance and area size of moving target, the best match is found to complete the tracking of moving target, and the phenomenon that the tracking object will be lost due to the occlusion between vehicles. In this paper, an area screening method is proposed to track the vehicle in different ways, and finally to obtain the moving track of the vehicle, which provides the basis for the judgement of abnormal events. In the aspect of abnormal event detection, by analyzing the moving track of the vehicle, the moving direction of the vehicle can be intuitively understood, and the moving direction of the vehicle can be compared with the positive direction of the road to judge the retrograde event of the vehicle. Traffic parameters such as instantaneous velocity, acceleration, change of center of mass position can be obtained indirectly by analyzing the moving track of vehicle, and the change of these traffic parameters can be used to judge whether the vehicle has illegal parking event or not. Based on the analysis of the dispersion of the distance between the vehicle track and the reference lane, whether the vehicle has changed track event or not is judged. In this paper, the actual traffic video sequences of different sections of highway are tested, and the experimental results show that the algorithm of automatic detection of abnormal events in this paper is effective, and can accurately detect the abnormal events of retrograde, parking and changing roads. It has good practicability.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491;TP391.41

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本文編號(hào)：1996936