本文關(guān)鍵詞：基于車輛跟蹤軌跡的停車和逆行檢測研究　出處：《長安大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 背景差分 車輛檢測 車輛跟蹤 卡爾曼濾波 停車和逆行檢測

【摘要】：隨著國民經(jīng)濟(jì)的快速發(fā)展,智能交通也越來越受到人們的重視。作為智能交通的重要組成部分,交通事件檢測對于緩解交通壓力、減少交通事故有著很重要的作用�；谝曨l的交通事件檢測技術(shù),由于其顯著的優(yōu)點(diǎn)(安裝簡單、覆蓋范圍廣、檢測直觀),越來越成為各國學(xué)者研究的重點(diǎn)。本文在前人研究的基礎(chǔ)上,重點(diǎn)研究了車輛檢測和車輛跟蹤,并根據(jù)質(zhì)心坐標(biāo)繪制運(yùn)動軌跡,之后利用車輛跟蹤軌跡分別對違章停車檢測和逆行檢測進(jìn)行了研究。首先,在運(yùn)動車輛檢測方面,通過比較常用的背景提取算法,采用多幀圖像平均法提取視頻背景。分析幀間差分法、光流法、背景差分法,考慮實(shí)際檢測效果,采用彩色背景差分法對車輛進(jìn)行檢測,之后對檢測圖像進(jìn)行二值分割,形態(tài)學(xué)處理和團(tuán)塊填充,采用HSV顏色空間對車輛陰影進(jìn)行去除,這一系列操作使車輛目標(biāo)能夠完整的提取出來。其次,在運(yùn)動車輛跟蹤方面,在介紹了常用跟蹤方法的基礎(chǔ)上,采用卡爾曼濾波和車輛質(zhì)心特征相結(jié)合的跟蹤方法,對下一時(shí)刻車輛特征值進(jìn)行預(yù)測,之后對車輛進(jìn)行搜索匹配,實(shí)現(xiàn)跟蹤。通過分析質(zhì)心坐標(biāo),提取出車輛跟蹤軌跡,并實(shí)時(shí)顯示在監(jiān)控畫面上面。在以上研究的基礎(chǔ)上,對違章停車和逆行進(jìn)行檢測識別。對于違章停車,采用質(zhì)心距離變化和速度變化進(jìn)行判定,當(dāng)質(zhì)心距離和車輛速度小于一定的閾值時(shí),結(jié)合具體停車時(shí)間進(jìn)行判定。對于逆行檢測,當(dāng)車輛行駛方向和道路規(guī)定方向相反時(shí),車輛質(zhì)心的Y軸坐標(biāo)變化來進(jìn)行判定。最后分別對違章停車算法和逆行算法進(jìn)行了視頻測試,取得了很好的效果。
[Abstract]:With the rapid development of national economy, people pay more and more attention to intelligent transportation. As an important part of intelligent transportation, traffic incident detection can relieve traffic pressure. It is very important to reduce traffic accidents. The video-based traffic incident detection technology has obvious advantages (simple installation, wide coverage, intuitive detection). On the basis of previous studies, this paper focuses on vehicle detection and vehicle tracking, and draws the motion track according to centroid coordinates. Then the vehicle tracking track is used to detect the illegal parking and the retrograde detection. Firstly, in the aspect of moving vehicle detection, the background extraction algorithm is compared. The multi-frame image averaging method is used to extract the video background, the inter-frame difference method, the optical flow method and the background difference method are analyzed. Considering the actual detection effect, the color background difference method is used to detect the vehicle. After the detection image binary segmentation, morphological processing and block filling, the use of HSV color space to remove the vehicle shadow, this series of operations make the vehicle targets can be extracted completely. Secondly. In the aspect of moving vehicle tracking, based on the introduction of common tracking methods, the Kalman filter and vehicle centroid feature tracking method are used to predict the vehicle feature value at the next moment. After the vehicle search matching to achieve tracking. Through the analysis of centroid coordinates to extract the tracking track of the vehicle and real-time display on the top of the monitoring screen. On the basis of the above research. For illegal parking, the change of centroid distance and the change of speed are adopted, when the centroid distance and vehicle speed are less than a certain threshold. For retrograde detection, when the driving direction of the vehicle is opposite to the specified direction of the road. The Y-axis coordinate of the center of mass of the vehicle is changed to judge. Finally, the algorithm of illegal parking and the retrograde algorithm are tested by video, and good results are obtained.
【學(xué)位授予單位】：長安大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U495;TP391.41

【共引文獻(xiàn)】

相關(guān)期刊論文 前2條

1 李孟歆;范靜靜;張穎;許偉靖;侯丁丁;;一種基于多重判別的運(yùn)動目標(biāo)檢測算法[J];沈陽建筑大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年04期

2 許宏科;秦嚴(yán)嚴(yán);;基于格拉布斯準(zhǔn)則的GMM背景建模方法[J];徐州工程學(xué)院學(xué)報(bào)(自然科學(xué)版);2015年02期

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本文編號：1430593