本文選題：動態(tài)背景建模 + 卡爾曼濾波��； 參考：《廣東工業(yè)大學(xué)》2014年碩士論文

【摘要】：目標(biāo)跟蹤技術(shù)被提出來已經(jīng)有幾十年的時間,經(jīng)過長時間的研究與發(fā)展,它已經(jīng)成為當(dāng)今社會非常重要的高端技術(shù),它在日常生活和軍事應(yīng)用領(lǐng)域都發(fā)揮著巨大的作用。此外,伴隨著計(jì)算機(jī)視覺技術(shù)的不斷發(fā)展,基于計(jì)算機(jī)視覺的車輛檢測與跟蹤技術(shù)成為智能交通系統(tǒng)的研究熱點(diǎn),是視頻監(jiān)控中動態(tài)目標(biāo)的流量統(tǒng)計(jì)、行為分析的重要理論基礎(chǔ)。對于動態(tài)背景環(huán)境下多目標(biāo)的檢測與跟蹤,仍舊是一個開放的研究領(lǐng)域,很多科研機(jī)構(gòu)和高等院校都對這一領(lǐng)域的研究投入了大量的時間和精力。本文主要研究了交通視頻監(jiān)控系統(tǒng)中運(yùn)動目標(biāo)的檢測與跟蹤技術(shù),對前景分割、車輛檢測、卡爾曼濾波、多目標(biāo)數(shù)據(jù)關(guān)聯(lián)等問題進(jìn)行了比較深入地研究。這一技術(shù)在智能交通監(jiān)控系統(tǒng)和智能視頻監(jiān)控系統(tǒng)中有著廣泛的應(yīng)用前景,具有極高的研究價值。 本文主要研究內(nèi)容包括以下幾個方面： 1、運(yùn)動車輛的檢測：首先介紹了幾種常用的前景分割算法,本文采用基于簡化KDE(Kernel Density Estimation)的動態(tài)背景建模方法,該方法能夠在復(fù)雜的背景環(huán)境下有效地提取出運(yùn)動前景,通過去噪和連通域處理,使得運(yùn)動車輛的檢測十分準(zhǔn)確和有效,為接下來的目標(biāo)跟蹤做準(zhǔn)備。 2、運(yùn)動車輛的跟蹤：在卡爾曼濾波算法的框架下進(jìn)行單目標(biāo)跟蹤的相關(guān)研究,在特征選擇部分,提出了一種rgI顏色直方圖,通過實(shí)驗(yàn)對比說明它相對于HSV(Hue,Saturation and Value)空間矩特征具有更好的跟蹤定位效果,能夠完成復(fù)雜背景環(huán)境下的目標(biāo)跟蹤過程。通過實(shí)驗(yàn)比較不同特征的跟蹤效果,驗(yàn)證了rgI顏色直方圖的優(yōu)越性,它能夠作為一種非常有效的特征對運(yùn)動目標(biāo)進(jìn)行描述。 3、多目標(biāo)數(shù)據(jù)關(guān)聯(lián)：本文通過對檢測到的多目標(biāo)提取全局特征并進(jìn)行特征匹配關(guān)聯(lián),建立相似函數(shù),對多個測量值進(jìn)行尋優(yōu)判斷,求得概率最大的測量值,并通過與相關(guān)閾值進(jìn)行比較來作為匹配結(jié)果,從而完成多目標(biāo)的跟蹤過程。 大量實(shí)驗(yàn)結(jié)果顯示,本文提出的跟蹤算法實(shí)時性高,魯棒性強(qiáng),能夠準(zhǔn)確處理真實(shí)交通場景環(huán)境下多車輛跟蹤,具有很高的應(yīng)用價值。
[Abstract]:Target tracking technology has been put forward for several decades. After a long time of research and development, it has become a very important high-end technology in today's society, it plays a great role in daily life and military applications. In addition, with the continuous development of computer vision technology, vehicle detection and tracking technology based on computer vision has become the research hotspot of intelligent transportation system, which is the traffic statistics of dynamic target in video surveillance. The important theoretical basis of behavior analysis. Detection and tracking of multi-targets in dynamic background environment is still an open research field. Many research institutions and universities have invested a lot of time and energy in this field. This paper mainly studies the technology of moving target detection and tracking in the traffic video surveillance system, and deeply studies the problems of foreground segmentation, vehicle detection, Kalman filter, multi-target data association and so on. This technology has a wide application prospect in intelligent traffic monitoring system and intelligent video surveillance system, and has high research value. The main contents of this paper include the following aspects: 1. Detection of moving vehicles: firstly, several common foreground segmentation algorithms are introduced. In this paper, a dynamic background modeling method based on simplified KDE(Kernel Density estimation is used, which can extract the motion foreground effectively in complex background environment. By denoising and connected domain processing, the detection of moving vehicles is very accurate and effective. 2. Tracking of moving vehicles: the research of single target tracking is carried out under the framework of Kalman filter algorithm. In the part of feature selection, a rgI color histogram is proposed. The experimental results show that it has a better tracking and localization effect than the spatial moment feature of HSV HueSaturation and, and it can complete the target tracking process in complex background environment. The advantages of rgI color histogram are verified by comparing the tracking effects of different features. It can be used as a very effective feature to describe moving targets. 3, multi-objective data association: by extracting the global feature and matching the feature to the detected multi-target, the similar function is established, and the most probabilistic measurement value is obtained. The multi-target tracking process is completed by comparing with the correlation threshold as the matching result. A large number of experimental results show that the proposed tracking algorithm is highly real-time robust and can accurately handle multi-vehicle tracking in real traffic scene environment. It has high application value.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP391.41;U495
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本文編號：1982975