本文選題：半監(jiān)督學(xué)習(xí) + 在線學(xué)習(xí)　； 參考：《華中科技大學(xué)》2016年博士論文

【摘要】：本課題來源于某**紅外跟蹤平臺的預(yù)研項目。在線紅外跟蹤是一個極具挑戰(zhàn)性的問題,在精確制導(dǎo)、飛行控制、無人機偵察、安防智能視頻監(jiān)控、基于視頻的人機交互以及智能視覺導(dǎo)航中都有著廣泛的應(yīng)用。由于紅外圖像相對于可見光圖像大多對比度較低,因此現(xiàn)有的大量視覺跟蹤方法在實際應(yīng)用時難以對紅外圖像中目標(biāo)進行快速準(zhǔn)確的檢測和魯棒的跟蹤。本文采用了近些年興起的基于檢測的跟蹤系統(tǒng)作為基本的紅外跟蹤模型。針對該跟蹤模型中的采樣標(biāo)記環(huán)節(jié),專注于研究通過減少標(biāo)記信息的錯誤來減弱在紅外跟蹤過程中出現(xiàn)的漂移問題,從而實現(xiàn)魯棒的在線紅外跟蹤。本文的主要貢獻如下：首先,本文基于充分性降維和半監(jiān)督學(xué)習(xí)首次提出了充分性半監(jiān)督特征提取理論,并設(shè)計了充分性半監(jiān)督紅外特征提取算法(fusion refinement:FR算法和semi-supervised kernel fusion refinement:SemKFR算法)。前一種算法提取的是線性特征,后一種算法提取的是非線性特征。相比于其他特征提取算法,本文提出的這兩種算法能夠同時利用部分置信度高的標(biāo)記信息和大量未標(biāo)記信息,且以最小化樣本信息損失為目標(biāo)來提取樣本特征,因此提取的特征具有更強的區(qū)別性。文中大量紅外圖像的實驗結(jié)果也證實了這兩種充分性半監(jiān)督特征提取算法的特征提取能力。其次,研究了基于半監(jiān)督粗糙公共向量(SRCV)的在線紅外跟蹤算法。該算法繼承了半監(jiān)督學(xué)習(xí)能夠部分標(biāo)記樣本的特性和判別公共向量(DCV)算法對于小樣本問題的魯棒性。在紅外跟蹤的初始階段,由于樣本匱乏,每個樣本(圖像塊)維度很高,這是典型的小樣本問題。經(jīng)研究發(fā)現(xiàn),本文提出的SRCV算法在樣本較少時仍能夠?qū)W習(xí)紅外目標(biāo)的低維特征。同時為了適應(yīng)在線紅外跟蹤,本文在SRCV算法的基礎(chǔ)上提出了增量式的SRCV在線特征提取算法(ISRCV).ISRCV算法利用隨機投影樹(RPTree)來近似紅外跟蹤過程中增量式采集樣本的內(nèi)部流形結(jié)構(gòu),同時借助于RPTree構(gòu)造了用于在線學(xué)習(xí)的目標(biāo)函數(shù),并提出了迭代更新準(zhǔn)則求解算法。實驗結(jié)果及分析表明,ISRCV提取的在線特征能夠有效保存紅外目標(biāo)的主要特征,在實驗中取得了與現(xiàn)有算法相近的跟蹤效果。再次,設(shè)計了基于邊緣信息的半監(jiān)督紅外特征表達算法(增量半監(jiān)督推廣的公共向量分析算法：ISSGCVA)。在利用基于檢測的跟蹤系統(tǒng)框架設(shè)計在線紅外目標(biāo)跟蹤的過程中,即使是采樣少量的標(biāo)記樣本也可能會存在標(biāo)記誤差。本文提出的ISSGCVA算法不需要指定每個樣本的具體標(biāo)記信息,而只用給定相似和不相似樣本對,就可以進行特征的學(xué)習(xí),從而能夠進一步減小標(biāo)記信息對于紅外跟蹤漂移的影響。同時,ISSGCCVA算法放寬了投影向量嚴(yán)格位于相似離散矩陣的零空間的約束,通過這種方式,本文的ISSGCVA算法既能夠處理紅外跟蹤初期的小樣本情形,又能夠處理較長時間跟蹤后的大樣本情形。此外,本文還詳細(xì)推導(dǎo)了一種有效的迭代算法來快速求解ISSGCVA算法的目標(biāo)函數(shù)。最后本文基于邊緣信息的ISSGCVA算法提出了一個在線紅外跟蹤系統(tǒng),并在實驗中證實了它的有效性。接著,本文針對基于檢測的跟蹤系統(tǒng)框架中的分類器模塊進行了研究,探索了一種改進的半監(jiān)督增量可變流形嵌入(ISemFME)分類算法。ISemFME算法繼承了半監(jiān)督學(xué)習(xí)能夠同時利用標(biāo)記和未標(biāo)記樣本進行分類模型的學(xué)習(xí),而且能夠在線的更新分類算法的參數(shù),以適應(yīng)紅外跟蹤過程中目標(biāo)外觀的不斷變化。同時,考慮到目標(biāo)外形的多樣性,ISemFME算法引入了回歸誤差函數(shù)來構(gòu)造目標(biāo)函數(shù),并且證明了該目標(biāo)函數(shù)是凸函數(shù),可以解析求解。為了適應(yīng)在線紅外跟蹤,本文還提出了緩沖策略(buffering strategy)來降低ISemFME算法的時間復(fù)雜度和空間復(fù)雜度。在VOT-TIR2015紅外數(shù)據(jù)庫上,基于ISemFME算法跟蹤系統(tǒng)表現(xiàn)出了較低的時間復(fù)雜度和很高的跟蹤準(zhǔn)確率。最后,由于紅外小目標(biāo)缺乏一定的外觀形狀,很多的特征提取算法并不能對紅外小目標(biāo)有很好的效果。本文針對紅外小目標(biāo)提取改進了一種融合圖像增強和目標(biāo)提取的算法—增強一比特變換(Enhanced one-bit transform:En1BT)算法,并且在實驗中驗證了該算法的有效性。本文圍繞基于檢測的跟蹤系統(tǒng),研究了紅外跟蹤過程中的兩個關(guān)鍵問題：如何提取魯棒的紅外目標(biāo)特征以及怎樣在觀測噪聲下構(gòu)造分類函數(shù),提出了基于半監(jiān)督學(xué)習(xí)的在線紅外跟蹤系統(tǒng),并在大量仿真實驗中得到了證明。本文所提出的在線半監(jiān)督紅外跟蹤理論、模型和算法對于其他的計算機視覺理論及應(yīng)用也有指導(dǎo)意義。
[Abstract]:This topic comes from a pre research project of an infrared tracking platform. The online infrared tracking is a very challenging problem. It has extensive applications in precision guidance, flight control, UAV reconnaissance, security intelligent video surveillance, video based human-computer interaction and intelligent visual navigation. Most of the existing visual tracking methods are difficult to fast and accurate detection and robust tracking of the target in the infrared image. In this paper, a tracking system based on detection in recent years is used as the basic infrared tracking model. The main contributions of this paper are as follows: firstly, this paper first proposed a sufficient semi supervised feature extraction theory based on sufficient reduced dimension and semi supervised learning, and designed the adequacy. The semi supervised infrared feature extraction algorithm (fusion refinement:FR algorithm and semi-supervised kernel fusion refinement:SemKFR algorithm). The previous algorithm extracts linear features and the latter algorithm extracts nonlinear characteristics. Compared with other feature extraction algorithms, the two algorithms proposed in this paper can use partial confidence simultaneously. High markup information and a large number of unlabeled information, and to minimize sample information loss as the target to extract the sample features, so the features extracted are more distinct. The experimental results of a large number of infrared images in this paper also confirm the feature extraction capability of the two full semi supervised feature extraction algorithms. Secondly, the study is based on semi supervision. The online infrared tracking algorithm of the governor rough common vector (SRCV). This algorithm inherits the characteristics of semi supervised learning which can partially mark the sample and discriminate the robustness of the common vector (DCV) algorithm for small sample problems. In the initial phase of the infrared tracking, the dimension of each sample (image block) is very high because of the lack of samples. This is a typical small sample. It is found that the proposed SRCV algorithm can still learn the low dimensional features of infrared targets when the sample is small. In order to adapt to the online infrared tracking, an incremental SRCV online feature extraction algorithm (ISRCV).ISRCV algorithm is proposed on the basis of the SRCV algorithm, and the random projection tree (RPTree) is used to approximate the infrared tracking. In the process, the internal manifold structure of the sample is incrementally collected. At the same time, the target function for online learning is constructed with the help of RPTree, and an iterative updating criterion is proposed. The experimental results and analysis show that the online features extracted by ISRCV can effectively preserve the main characteristics of the infrared target, which is similar to the existing algorithms in the experiment. Thirdly, a semi supervised infrared feature expression algorithm based on edge information (incremental semi supervised generalized vector analysis algorithm: ISSGCVA) is designed. In the process of designing an online infrared target tracking based on a detection based tracking system framework, even a small number of labeled samples may also have markup errors. The ISSGCVA algorithm proposed in this paper does not need to specify the specific labeling information of each sample, but only a given similar and dissimilar sample pair can be used for characteristic learning, which can further reduce the influence of the label information on the infrared tracking drift. At the same time, the ISSGCCVA algorithm relaxes the projection vector to be strictly located in the similar discrete matrix. In this way, the ISSGCVA algorithm in this paper can not only deal with small sample cases in the initial stage of infrared tracking, but also can handle large sample cases after a long time tracking. In addition, an effective iterative algorithm is also derived to quickly solve the target function of the ISSGCVA algorithm. Finally, this paper is based on the edge information. The ISSGCVA algorithm proposed an online infrared tracking system and proved its effectiveness in the experiment. Then, this paper studied the classifier module in the framework of detection based tracking system, and explored an improved semi supervised incremental variable manifold embedding (ISemFME) algorithm.ISemFME algorithm to inherit the semi supervised learning. At the same time, learning can use markers and unlabeled samples for classification model learning, and can update the parameters of the classification algorithm online to adapt to the constant changes in the appearance of the target in the infrared tracking process. At the same time, considering the diversity of the target shape, the ISemFME algorithm introduces the return error function to construct the target function, and proves that the target function is constructed. The objective function is a convex function, which can be solved analytically. In order to adapt to the online infrared tracking, this paper also proposes a buffer strategy (buffering strategy) to reduce the time complexity and space complexity of the ISemFME algorithm. On the VOT-TIR2015 infrared database, the ISemFME algorithm tracking system shows a lower time complexity and high degree. In the end, because of the lack of a certain shape of the small infrared target, many feature extraction algorithms do not have a good effect on the small infrared targets. In this paper, an improved algorithm of image enhancement and target extraction, enhanced Enhanced one-bit transform:En1BT algorithm, is improved for infrared small target extraction. In this paper, the effectiveness of the algorithm is verified in the experiment. In this paper, two key problems in the infrared tracking process are studied around the detection based tracking system: how to extract the robust infrared target features and how to construct the classification function under the observed noise, and put forward an online infrared tracking system based on semi supervised learning. It is proved that the on-line semi supervised infrared tracking theory, the model and the algorithm are also instructive to other computer vision theories and applications.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TN219;TP391.41

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