摘要
基于视觉的目标跟踪一直以来都是计算机视觉领域一个重要而富有挑战性的研究热点,它涉及到模式识别、图像处理、人工智能、计算机应用等诸多方面的知识,随着高性能计算机和高质量且廉价的摄像终端的增多,以及自动视频分析需求量的增大,视觉目标跟踪算法引起人们越来越多的关注,并且在军事和民用的许多领域(如：智能监控系统、智能交通系统、精确制导系统、智能医学诊断)等都具有极为广泛的应用前景。在过去的几十年中很多优秀的视觉目标跟踪算法及有效的新理论被相继提出,然而,由于通常的视频序列中存在着内因(如：尺度变化,姿势变化和形状变化等)和外因(如：部分或全部遮挡,光照变化,运动模糊和背景杂乱)等因素的干扰,要想设计一个具有普适性的实时的、鲁棒的、精准稳定的视觉目标跟踪系统来满足实际需求,仍然面临着很大的挑战。
针对以上问题,本论文在详细分析传统视觉目标跟踪方法的基础上,以生成模型和判别模型理论为指导,结合当前国际前沿动态和实际应用需求,提出了一些新的思想和方法。本文主要是在复杂场景中、单摄像采集设备且伴随运动的情况下,对单目标跟踪算法展开研究,其目的是为了有效地丰富视觉目标跟踪理论以及提高视觉目标跟踪的鲁棒性和准确性。本论文研究的主要内容和创新点如下：
(1)以判别模型为基础,提出了一个协同训练框架下变权重实时视觉目标跟踪系统。如何提升分类器的分类性能是判别跟踪方法研究的主要目标,而提取有效的特征来描述目标外观是构建分类器的关键所在,因此本文在压缩跟踪器的基础上引入了一种有效的特征选择策略,来剔除冗余信息。该方法采用Anyboost泛函梯度下降方法,能够有效地选取出最具判别力的特征来构建分类器。在构建分类器的时候需要采用正、负样本,如果赋予样本相同的权重而不区分样本的重要性,那么在某种程度上分类器的分类性能将会下降,本论文提出了一种有效的样本加权方法,把样本的重要性引入到判别跟踪系统的在线学习过程中来。自学习方法是仅采用一种特征来对跟踪系统进行建模,当目标的外观不能被有效的表示或者跟踪系统出现微小的错误时,随着时间推移跟踪器将积累这些错误而导致跟踪失败。本文采用特征融合的方法来构建协同训练框架,利用灰度特征和局部二进制纹理特征独立构建两个分类器进行相互学习、相互更新,然后把各自分类器的分类结果融合一起,通过加权的形式获取最优跟踪结果。
(2)以生成模型为基础,提出了一种在粒子滤波框架下基于偏最小二乘和稀疏学习的实时视觉目标跟踪系统。有效的目标外观模型表示方法,将对跟踪系统起到决定性的作用。通常的生成跟踪器仅采用目标的前景信息对目标外观进行建模而忽略了目标背景信息的作用,本论文提出了一种有效的目标外观建模方法,该方法采用偏最小二乘理论充分利用前景和背景信息构建一个判别特征子空间,并把稀疏表示理论引入到此特征子空间上来对目标外观进行建模,该方法比起传统的基于稀疏表示理论的跟踪算法,具有较低的计算复杂度,因此可以实时处理高分辨率影像。遮挡问题是视觉目标跟踪中常见的且又极难处理的问题。本论文还提出了一种有效的遮挡检测机制,该遮挡检测机制是在稀疏表示理论框架下,采用稀疏解得到的‘琐碎’模板构建的,它能对被跟踪的目标进行实时检测,并对检测到的遮挡或者异常值进行实时处理。
Visual target object tracking has always been an important yet challenging hot topic in the field of computer vision, it involves many aspect knowledge including pattern recognition, image processing, artificial intelligence, computer application and so on, with the increase of high performance computer and high quality and cheap camera, and with the increase of demand for automatic video analysis, visual target object tracking algorithms take more and more attention, in many fields of military and civilian have extremely extensive application prospect,(such as:(Intelligent monitoring system, intelligent transportation system, precision guidance system, intelligent medical diagnosis, etc.) Over the past few decades, lots of excellent visual target tracking algorithms and effective new theory have been proposed, however, due to many difficulties account for intrinsic factors (e.g. scale and pose change, shape deformation) and extrinsic factors (e.g. partial or total occlusion, illumination change, cluttered background and motion blur), in order to design an universal visual target object tracking system, which has the attribute of real-time, robust, accurate and stable, is still facing great challenges to meet the actual demand.
To solve these problems, in this paper, we detailed analysis and study traditional visual target object tracking method, and generative model and the discriminative model theory as a guide. It combines academic frontiers dynamic and practical application demand, and proposes some new idea and methods to enrich visual target object tracking theory and effectively improve the accuracy and robustness of object tracking system. In this paper, we study the object tracking system mainly in complex environment with single video device yet movement. The main contents and contributions of this dissertation are summarized as follows:
(1) We propose an adaptive weighted real-time compressive tracking system in co-training framework based on discriminative model. How to improve classification performance of a classifier is the main goal of the discriminative tracking method. Building a classifier, it is critical to extract the effective features to describe the target appearance. In this paper, we introduce an effective feature selection criterion in compressive tracker to eliminate redundant information. Our method adopts Anyboost functional gradient descent method to effectively choose the most discriminative features to build classifier. We use positive and negative samples to build classifier, if these samples have the same weight and not discriminative, the classification performance of classifier will be degraded to some degree. In this paper, we integrate the sample importance into compressive tracker online learning procedure and proposed a weighted method. The self-learning often suffer from drift due to it merely make use of one kind of feature to model tracking system, when the model can not effectively describe the object appearance or the tracking system has a minor error, then the appearance model ends up getting updated with a suboptimal positive example. Over time, the errors will be accumulated and can cause drift (failure). In this paper, we adopt feature fusion method to build co-training framework. We use gray feature and LBP texture feature to build two classifiers independently to update and learn each other, then we obtain an optimal tracking result by weighting classification results.
(2) We propose an effective and efficient online visual tracking algorithm with an appearance model based on partial least squares and sparse learning (PLSSL) in a particle filter framework. It is prerequisite for visual target object tracking to effectively describe the object appearance. Traditional generative tracker often only adopts foreground information to model the object appearance and neglect the background information. In this paper, a low-dimensional discriminative feature subspace can be obtained via partial least squares method from high-dimensional feature space which consists of a few collected positive and negative samples. Then, we introduce popular sparse learning into PLS methods to model the candidate target object whose appearance is represented by the PLS subspace. Compared with popular sparse representation based tracking algorithms, our algorithm can process higher resolution image observations and handle challenging image sequences with drastic appearance change and background clutters. The partial occlusion problem is one of the most general yet challenging problems which remain as arguably the most critical factor for causing tracking failures. In this paper, we proposed an effective occlusion detection mechanism based on sparse representation theory framework. We make use of trivial coefficients to construct an effective online update mechanism to real-time detect and process partial occlusion or outliers
引文
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