本文關(guān)鍵詞： 碼本 混合高斯模型 梯度統(tǒng)計(jì)直方圖 稀疏表示 塊正交匹配 馬爾可夫隨機(jī)場　出處：《長安大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：交通監(jiān)控場景中情況各異、環(huán)境復(fù)雜，運(yùn)動(dòng)目標(biāo)的任意性和隨機(jī)性，以及光照、遮擋、目標(biāo)姿態(tài)等不確定性，使運(yùn)動(dòng)目標(biāo)檢測與跟蹤過程中出現(xiàn)的問題具有不可預(yù)測性。本文在復(fù)雜交通監(jiān)控場景中應(yīng)用單目攝像機(jī)獲取目標(biāo)區(qū)域，建立借助目標(biāo)特征實(shí)現(xiàn)目標(biāo)檢測與跟蹤方案。圍繞形變與尺度變化下目標(biāo)的檢測與跟蹤方法，研究了視頻中目標(biāo)區(qū)域的檢測和提取、基于目標(biāo)特征的識別與分類，以及復(fù)雜場景中尺度變化的目標(biāo)的跟蹤問題，這些問題形成了基于復(fù)雜交通監(jiān)控場景下運(yùn)動(dòng)目標(biāo)檢測與跟蹤方法的技術(shù)研究。 論文的主要內(nèi)容如下： 1)提出一種基于量子聚類分析技術(shù)的像素塊編碼的交通背景提取方法。實(shí)際交通監(jiān)控中的場景狀況是不確定的，為了精確的提取前景需要用有效的方法對背景建立模型，了解視頻序列中像素變化并考慮到像素之間的相互關(guān)系。本文在經(jīng)典Codebook方法的基礎(chǔ)上進(jìn)行探索和研究，將視頻圖像劃分為像素塊，對像素塊進(jìn)行聚類學(xué)習(xí)和編碼，在該編碼的基礎(chǔ)上用交替學(xué)習(xí)和更新的方法對編碼進(jìn)行實(shí)時(shí)更新。實(shí)驗(yàn)證明，本方法獲取的前景干擾較少，目標(biāo)區(qū)域圖像較清晰；另外該方法計(jì)算簡單，加之采用交替更新的方法，實(shí)時(shí)性好，提取的前景具有較好的魯棒性。 2)提出一種基于MRF的自適應(yīng)車輛陰影檢測和消除方法。對車輛陰影通過將前景與背景像素的顏色和局部紋理特征進(jìn)行對比。顏色特征是用HSI顏色空間特性，局部紋理則用SILTP編碼的漢明距離對陰影像素檢測，其中得閾值用極大似然估計(jì)的方法估計(jì)。在以上特征的基礎(chǔ)上，用馬爾可夫隨機(jī)場對像素標(biāo)記及其鄰域的相關(guān)性進(jìn)行表示，進(jìn)而對陰影和非陰影像素進(jìn)行分割。實(shí)驗(yàn)結(jié)果表明，和其他方法相比該方法有相似或者更優(yōu)越的性能，能適應(yīng)光照的變化環(huán)境。 3)提出基于Gabor特征圖像上提取HOG特征的行人識別方法(在此簡稱GHOG方法)。針對場景中目標(biāo)姿態(tài)、光線等不斷變化的需求，該方法將視頻圖像和Gabor小波進(jìn)行卷積，得到的40個(gè)小波圖像進(jìn)行尺度和方向上進(jìn)行融合，形成一幅Gabor的融合圖像；在此基礎(chǔ)上進(jìn)行HOG特征提取，根據(jù)提取的HOG特征用Real Adaboost級聯(lián)分類的方法進(jìn)行目標(biāo)識別。實(shí)驗(yàn)證明，該方法能有效降低錯(cuò)誤檢測率；對目標(biāo)在Gabor特征圖像融合過程中采取了編碼的方式，使計(jì)算量也有效降低。 4）提出一種對于目標(biāo)圖像分塊稀疏表示和貝葉斯估計(jì)進(jìn)行目標(biāo)跟蹤的方法。針對目標(biāo)跟蹤過程中遮擋問題，該方法根據(jù)基礎(chǔ)樣本庫子空間的塊對目標(biāo)的外觀進(jìn)行稀疏線性組合表示，為了實(shí)時(shí)更新目標(biāo)模板，采用了增量學(xué)習(xí)的方法來適應(yīng)不斷變化的目標(biāo)。然后建立了基于重建圖像和觀察目標(biāo)的近似誤差的概率觀察模型，這個(gè)觀察模型用一個(gè)隨機(jī)的仿射運(yùn)動(dòng)模型形成粒子濾波進(jìn)行目標(biāo)跟蹤。本文提出的跟蹤方法比IVT和L1T方法在處理遮擋、姿態(tài)變化較大、突然光線變化和尺度變化方面有較好的效果。
[Abstract]:In the traffic surveillance scene is different, the environment is complex, arbitrary and random moving target, and illumination, occlusion, object pose uncertainty, so that the moving target detection and tracking problems in the process of unpredictability. Get the target area using monocular camera based on complex traffic monitoring scene, establish with the help of the target characteristics to achieve target detection and tracking scheme. Based on the method of detecting and tracking target deformation and scale change, studies the detection and extraction of the target area in the video, recognition and classification based on the features of the target tracking problem, and target scale in complex scene changes, these problems form the technology of detection and tracking method moving target in complex traffic monitoring based on the scene.
The main contents of the paper are as follows:
1) extraction method is proposed for analysis of quantum clustering technology based on pixel block encoding traffic background. The actual traffic monitoring scene in the situation is uncertain, in order to accurately extract the foreground with the effective method for background modeling, pixels in video sequence changes and considering the relationship between pixels in the article. The exploration and Research Based on the classical Codebook method, the video image is divided into blocks of pixels, clustering learning and encoding of pixel blocks, updated the encoding method by alternative learning and updating based on the encoding. Experiments show that this method gets the prospect of less interference, the target region image is clear; the method is simple, and the method of renewal, good real-time performance, the extraction of foreground is robust.
2) proposed an adaptive vehicle shadow detection and elimination method based on MRF. The vehicle shadow through the foreground and background pixel color and local texture features were compared. The color feature is characteristic of HSI color space, local texture with SILTP encoding the Hamming distance of shadow pixel detection, the threshold method for maximum likelihood estimated. Based on these features, represented by a Markov random field on correlation between pixel labeling and its neighbor, and the shadow and non shadow pixel segmentation. The experimental results show that compared with other methods this method has similar or better performance, can adapt to the changes of illumination.
3) proposed pedestrian recognition method of HOG feature extraction based on Gabor image features (here referred to as GHOG). According to the attitude of the target in the scene, the light and the changing needs of the video image and Gabor wavelet convolution, 40 wavelet image obtained by the scale and direction of fusion, forming a fusion image Gabor; based on HOG feature extraction, HOG feature extraction method based on the Real Adaboost cascade classifier for target recognition. The experimental results show that this method can effectively reduce the error detection rate; the target in the Gabor feature in the process of image fusion by encoding, the calculation is reduced effectively.
4) proposed a target image for block sparse representation and Bias estimation for target tracking. The occlusion problem in the process of target tracking, the method based on sample subspace block to target the appearance of sparse linear combination, in order to update the target template, using incremental learning method to adapt to the the change of target. Then established observation model of reconstructed image and the observed object approximation error probability based on the observation model with a random affine motion model into particle filter for target tracking. The tracking method proposed in this paper than the IVT and L1T methods in dealing with occlusion, attitude changes greatly, has good effect of a sudden change of light and scale changes.

【學(xué)位授予單位】：長安大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TP391.41;U495

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相關(guān)期刊論文 前1條

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本文編號：1550005