目标跟踪算法研究
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摘要
本文对目标跟踪算法展开了全面而又不失深入的探讨和研究。第一章阐述了目标跟踪算法的研究背景,以及研究意义,并在民用和军事等层次上探讨了目标跟踪技术的应用前景,展示了它丰富的、具有吸引力的应用价值；第一章也讨论了目标跟踪技术的国内外研究现状,将已有的各种跟踪算法按照其内在跟踪思想分成了自顶向下和自低向上的两个类别,进行了深入浅出的分析；第一章最后提取出跟踪问题中普遍存在的两个难题：1)如何准确、及时地学习到目标的形态变化；2)如何有效地描述目标,引出了本文围绕这些难题所展开研究的出发点：引入新工具、像认识目标一样认识背景和先验知识的嵌入。第二章围绕着基于局部特征点的目标跟踪算法展开研究。对局部特征点的简要描述展示了这种新工具所提供的很多出色性能,如丰富性、独特性、稳定性(可再现性)等等；然后对此章中心-SURF跟踪的概述勾勒出了该算法的大致轮廓；同时,第二章也讨论了相关的跟踪算法,比较了它们与SURF跟踪算法的区别。接下来,第二章详细地描述了SURF跟踪的算法细节,讨论了算法核心的特征运动生成模型以及在线EM算法对模型参数的学习更新,并对遮挡问题进行了讨论。此章实验部分用充足的试验结果展示了SURF跟踪算法的优异性能,以及在处理各种挑战性场景时的健壮表现；同时,此章实验部分还将SURF跟踪与集成跟踪进行了比较,验证了算法的优越性；此章实验部分也同样验证了算法对遮挡问题处理机制的正确性。第三章围绕基于水平集的跟踪算法展开研究。对水平集理论的介绍,以及对其数值计算方法的讨论展示出此方法在描述轮廓结构上的强大能力；随后,第三章讨论了现有的基于水平集方法的跟踪算法,讨论了它们的跟踪思想以及算法性能；接着,提出了此章中心-嵌入水平集的集成跟踪算法,并对算法的细节展开深刻地讨论。此章实验部分非常详细地展示了算法的细节,并突出显示了水平集函数在跟踪过程中对目标轮廓的精确描述。此章实验部分也同样比较了经典的集成跟踪和嵌入水平集的跟踪算法,利用经典集成跟踪的失败反衬出水平集这一工具为算法的精确性、鲁棒性带来的正面影响。最后一章对全文做出总结,并对目标跟踪技术未来的发展做出展望。总而言之,本文的主要研究成果和创新可以概括为以下三点：
1.对现有的目标跟踪算法进行了分类,按照其内在思想将现有跟踪算法分为自顶向下和自低向上两类；并在此分类指导下,对很多经典的跟踪算法进行了讨论和分析,比较了它们的共同点和差异之处,分析了它们在处理实际场景时失效的状况和原因。
2.创新地提出了SURF跟踪算法；SURF跟踪的内在思想是：观测得到目标局部特征点的运动信息,并以此推测出目标整体的运动参数；在SURF跟踪的算法框架中,目标局部特征点运动与目标整体运动之间的关联通过生成模型很好地得到刻画,并且在线EM算法实时地学习模型参数,保证了生成模型所刻画出来的这种局部与整体之间关联的正确性。同时,算法框架还为主动遮挡检测提供了可能,并在实验中得到了验证。
3.对经典的集成跟踪进行改进,提出了嵌入水平集的集成跟踪算法；水平集函数作为一种高维的轮廓表示方式,在描绘轮廓的演化过程上有着强大的能力；嵌入水平集的的集成跟踪算法将水平集轮廓演化和集成跟踪无缝地融合在一起,使得两者相互促进,使得跟踪的精确度和健壮度都得到很大的提高,在实验中收到了很好地效果。
This thesis has conducted comprehensive discussions and researches on object tracking algorithms without losing deep insights. The first chapter expounded the research back-ground of object tracking as well as its significance, discussed its prospect for applications on the civil and military affairs, displayed its rich and attractive value in practice; In the first chapter, the status quo of object tracking research at home and abroad was also dis-cussed, and various existing tracking algorithms were categorized by their latent ideas into two classes:Top-Down and Down-Top, and were expounded detailedly in simple language; The last part of first chapter exposed two problems which had prevailed in object tracking technical world:1) How to represent the object effectively; 2) How to learn the changes of object appearance correctly and timely, brought out the focus of research of this thesis:1) introduce new technical tools into tracking system; 2) understand background by the way of we understanding object; 3) Embed priori knowledge. The second chapter focused on the feature point based tracking algorithms; Firstly, It expounded concisely the characteristic of local feature points, such as richness, distinctiveness and robustness; Then, It gave a brief sketch of SURF tracking algorithm, and discussed relative algorithms and their differences with SURF tracking; In the next paragraphs, it displayed algorithm details of SURF track-ing, explained the feature motion generative model which plays a core role in the framework, demonstrated the online EM algorithm for learning model parameters, and also talked over the problem of occlusion. Substantial experiment results showed in this chapter verified the outstanding performance of SURF tracking algorithm, specially in dealing with various chal-lenging cases. The correctness of SURF tracking's mechanism for active occlusion detection was also proved in the experiment part. The third chapter aimed to discuss level set based tracking algorithms; In the beginning it introduced theory of level set and its numerical com-puting methods, and talked about the existing level set based tracking algorithms and their capabilities; In the next level set embedded ensemble tracking algorithm, as the core of this chapter, was described detailedly. Experiments for this chapter highlighted the capability of level set method to precisely represent object contour.
Sum up, the main study achievement and innovation of this thesis could be concluded as the following points:
1. By their latent ideas, we categorized various existing tracking algorithms into two classes:Top-Down and Down-Top; Under the proposed classification criteria, we dis-cussed a great number of prevailing tracking algorithms, analyzed their common points as well as differences, and also demonstrated their failure situations and reasons.
2. Innovatively, we proposed SURF tracking algorithm; Its latent idea is that:estimat-ing object global motion parameters by motion observations of local feature points of object; In the SURF tracking framework, the relationship between object local feature points'motions and object global motion was depicted by generative model, and an online EM algorithm was employed to learn model parameters. Meanwhile, an active occlusion detection mechanism was proposed and verified in the experiments.
3. We improved the classical ensemble tracking, proposed level set embedded ensemble tracking algorithm; As a high dimension representation of contour, level set function had great power in describing evolution process of contour; Level set embedded en-semble tracking algorithm seamlessly integrated level set function and ensemble track-ing together, made them promoted by each other; It was proved in the experiments that tracking accuracy and robustness was greatly improved by the new algorithm.
1.对现有的目标跟踪算法进行了分类,按照其内在思想将现有跟踪算法分为自顶向下和自低向上两类；并在此分类指导下,对很多经典的跟踪算法进行了讨论和分析,比较了它们的共同点和差异之处,分析了它们在处理实际场景时失效的状况和原因。
2.创新地提出了SURF跟踪算法；SURF跟踪的内在思想是：观测得到目标局部特征点的运动信息,并以此推测出目标整体的运动参数；在SURF跟踪的算法框架中,目标局部特征点运动与目标整体运动之间的关联通过生成模型很好地得到刻画,并且在线EM算法实时地学习模型参数,保证了生成模型所刻画出来的这种局部与整体之间关联的正确性。同时,算法框架还为主动遮挡检测提供了可能,并在实验中得到了验证。
3.对经典的集成跟踪进行改进,提出了嵌入水平集的集成跟踪算法；水平集函数作为一种高维的轮廓表示方式,在描绘轮廓的演化过程上有着强大的能力；嵌入水平集的的集成跟踪算法将水平集轮廓演化和集成跟踪无缝地融合在一起,使得两者相互促进,使得跟踪的精确度和健壮度都得到很大的提高,在实验中收到了很好地效果。
This thesis has conducted comprehensive discussions and researches on object tracking algorithms without losing deep insights. The first chapter expounded the research back-ground of object tracking as well as its significance, discussed its prospect for applications on the civil and military affairs, displayed its rich and attractive value in practice; In the first chapter, the status quo of object tracking research at home and abroad was also dis-cussed, and various existing tracking algorithms were categorized by their latent ideas into two classes:Top-Down and Down-Top, and were expounded detailedly in simple language; The last part of first chapter exposed two problems which had prevailed in object tracking technical world:1) How to represent the object effectively; 2) How to learn the changes of object appearance correctly and timely, brought out the focus of research of this thesis:1) introduce new technical tools into tracking system; 2) understand background by the way of we understanding object; 3) Embed priori knowledge. The second chapter focused on the feature point based tracking algorithms; Firstly, It expounded concisely the characteristic of local feature points, such as richness, distinctiveness and robustness; Then, It gave a brief sketch of SURF tracking algorithm, and discussed relative algorithms and their differences with SURF tracking; In the next paragraphs, it displayed algorithm details of SURF track-ing, explained the feature motion generative model which plays a core role in the framework, demonstrated the online EM algorithm for learning model parameters, and also talked over the problem of occlusion. Substantial experiment results showed in this chapter verified the outstanding performance of SURF tracking algorithm, specially in dealing with various chal-lenging cases. The correctness of SURF tracking's mechanism for active occlusion detection was also proved in the experiment part. The third chapter aimed to discuss level set based tracking algorithms; In the beginning it introduced theory of level set and its numerical com-puting methods, and talked about the existing level set based tracking algorithms and their capabilities; In the next level set embedded ensemble tracking algorithm, as the core of this chapter, was described detailedly. Experiments for this chapter highlighted the capability of level set method to precisely represent object contour.
Sum up, the main study achievement and innovation of this thesis could be concluded as the following points:
1. By their latent ideas, we categorized various existing tracking algorithms into two classes:Top-Down and Down-Top; Under the proposed classification criteria, we dis-cussed a great number of prevailing tracking algorithms, analyzed their common points as well as differences, and also demonstrated their failure situations and reasons.
2. Innovatively, we proposed SURF tracking algorithm; Its latent idea is that:estimat-ing object global motion parameters by motion observations of local feature points of object; In the SURF tracking framework, the relationship between object local feature points'motions and object global motion was depicted by generative model, and an online EM algorithm was employed to learn model parameters. Meanwhile, an active occlusion detection mechanism was proposed and verified in the experiments.
3. We improved the classical ensemble tracking, proposed level set embedded ensemble tracking algorithm; As a high dimension representation of contour, level set function had great power in describing evolution process of contour; Level set embedded en-semble tracking algorithm seamlessly integrated level set function and ensemble track-ing together, made them promoted by each other; It was proved in the experiments that tracking accuracy and robustness was greatly improved by the new algorithm.
引文
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