基于广义Hough变换的目标跟踪算法研究
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摘要
视频图像序列中的目标跟踪是计算机视觉领域的热点问题,广泛地应用于视频监控、视频检索、车辆导航、人机交互、体育比赛直播等场合。视频目标跟踪主要的研究方向是对动物视觉行为的模拟和对人类分析思维模式的抽象；为此,研究者对视觉特征和分析方法都进行了逼近,涌现出了许多能解决特定问题的有效算法。但由于真实世界环境的复杂性和被跟踪目标本身的动态性,依然存在很许多亟待解决的困难,背景环境的干扰、光照变化的因素、目标之间的交错和遮挡、目标外观和姿态的变化等都是影响目标跟踪算法性能的重要挑战。
本文在对现有的目标跟踪技术进行充分学习和理解的基础上,深入研究了广义Hough变换在目标跟踪算法中的应用,讨论了目标检测分类器的设计,目标特征的提取和描述,目标模型的在线更新,目标位置的估计方法,以及遮挡推理,多目标关联模型等重点技术问题,提出了一系列有效的解决方法。本文的主要研究成果如下：
(1)提出了半监督Hough Forests目标跟踪方法。以Hough Forests为基本学习框架,引入了基于跟踪过程中得到的样本个体信息和空间分布信息的随机标签,提高了分类器的分类性能,并降低了同类目标的混淆概率；采用了类似粒子滤波的随机采样方式对分类器的检测和更新环节进行加速,提高了算法的实时性。
(2)提出了一种基于检测反馈的长程跟踪算法。将闭环控制的思想引入到视觉跟踪问题中,用以光流向量作为特征的前馈跟踪控制器,并用与之特征互补的检测算子作为反馈控制器,用反馈的结果对跟踪算子进行调节,并对目标的外观模型进行维护。该算法能够大幅提高跟踪算法的鲁棒性和稳定性,对长时间轴上的目标跟踪应用非常有效。
(3)提出了基于双层粒子滤波的半监督Hough Forests多目标跟踪算法。用双层粒子滤波在统一的随机采样框架中解决样本采样和位置采样两个随机过程,提高了检测算子的实时性能。并设计了一个多目标维护机制应对遮挡、背景变化、目标进出场景等可能引起目标混淆的情况。
(4)提出了一种基于多视角多片段Hough Forests算子和在线条件随机域(CRF)模型的多目标行人实时跟踪算法。将人体目标划分为多个视角下的多个片段(Multi-Part),在线学习各个片段的外观模型,增强了模型的鲁棒性和抗遮挡能力。通过在线CRF模型建立目标在较短的滑动时间窗口上的全局关联,能够在复杂的背景中解决多个目标之间的遮挡和混淆。
Object tracking in video sequence is a popular topic in computer vision area. The use of object tracking is pertinent in the tasks of:automated surveillance, video retrieval, auto-navigated vehicle, human-computer interaction, sport live broadcast, etc. The general researching path of object tracking is to simulate human vision behavior and to imitate the inference mechanism of human being. Great quantities of approaches, from visual feature to analysis method, are proposed on this researching path. As we can see, numerous algorithms have been presented on some particular applications, and have been proved to be effective. However, common solutions or uniform frameworks for most problems are still not available, due to the complicated circumstance of real world and the changeability of tracked object itself. Some crucial challenges need to be settled, such as background noises, illumination changes, inter-object occlusions, object's appearance and pose changes.
After thoroughly studied on the art-of-state object tracking algorithms, this dissertation intensively investigated applications of Generalized Hough Transform in object tracking field, discussed some key issues on this topic and proposed a series of effective solutions, including classifier design, visual feature selection, online model updating, location estimation, occlusion reasoning, and multi-object association, etc. The major achievements include:
(1) Semi-supervised Hough Forest Tracking Method. Proposed a flexible semi-supervised learning method based on Hough forests classifier. Introduced a random label distribution, which is based on spatial consistency in context and object-specific information detected in tracking procedure, to improve the performance of classifier, and reduce confusion between inner-class objects. Meanwhile, a Particle-Filtering-Kind random sampling scheme was implemented in detection and updating phases, which can accelerate the whole tracking procedure.
(2) Detection feedback based long-term tracking algorithm. Introduced close-loop idea into tracking field. Used optic-flow based tracker as the feedforward controller, and a detector based on complementary features as feedback controller. Then the latter one's result was used to adjust the tracker's parameters and to maintain the appearance model of the tracked object. This feedback scheme dramatically increased the robustness and stability of tracking system on long-time axis.
(3) Multiple objects tracking with Dual-level Particle Filter Embedded Semi-supervised Hough Forests. Used a dual-level particle filter, which disposed patch random sampling and position random sampling processes in one uniform sampling framework, raised time performance of the detector. And an objects maintenance scheme was designed out to cope with occlusion, background changing, entry/exit and so on.
(4) Multi-pedestrian real-time tracking with multi-view multi-part Hough Forests Detector and on-line Conditional Random Field(CRF) model. Divided pedestrian object into multiple parts, learned full body appearance models and parts appearance models respectively based on online Hough Forests algorithm, which improve the model's robustness and stability against occlusion. And a CRF model was employed to formulate dependencies and affinities between tracklets and detection responses in a short time sliding window to represent current global association, which could handle occlusion and confusion in complex scences well.
本文在对现有的目标跟踪技术进行充分学习和理解的基础上,深入研究了广义Hough变换在目标跟踪算法中的应用,讨论了目标检测分类器的设计,目标特征的提取和描述,目标模型的在线更新,目标位置的估计方法,以及遮挡推理,多目标关联模型等重点技术问题,提出了一系列有效的解决方法。本文的主要研究成果如下：
(1)提出了半监督Hough Forests目标跟踪方法。以Hough Forests为基本学习框架,引入了基于跟踪过程中得到的样本个体信息和空间分布信息的随机标签,提高了分类器的分类性能,并降低了同类目标的混淆概率；采用了类似粒子滤波的随机采样方式对分类器的检测和更新环节进行加速,提高了算法的实时性。
(2)提出了一种基于检测反馈的长程跟踪算法。将闭环控制的思想引入到视觉跟踪问题中,用以光流向量作为特征的前馈跟踪控制器,并用与之特征互补的检测算子作为反馈控制器,用反馈的结果对跟踪算子进行调节,并对目标的外观模型进行维护。该算法能够大幅提高跟踪算法的鲁棒性和稳定性,对长时间轴上的目标跟踪应用非常有效。
(3)提出了基于双层粒子滤波的半监督Hough Forests多目标跟踪算法。用双层粒子滤波在统一的随机采样框架中解决样本采样和位置采样两个随机过程,提高了检测算子的实时性能。并设计了一个多目标维护机制应对遮挡、背景变化、目标进出场景等可能引起目标混淆的情况。
(4)提出了一种基于多视角多片段Hough Forests算子和在线条件随机域(CRF)模型的多目标行人实时跟踪算法。将人体目标划分为多个视角下的多个片段(Multi-Part),在线学习各个片段的外观模型,增强了模型的鲁棒性和抗遮挡能力。通过在线CRF模型建立目标在较短的滑动时间窗口上的全局关联,能够在复杂的背景中解决多个目标之间的遮挡和混淆。
Object tracking in video sequence is a popular topic in computer vision area. The use of object tracking is pertinent in the tasks of:automated surveillance, video retrieval, auto-navigated vehicle, human-computer interaction, sport live broadcast, etc. The general researching path of object tracking is to simulate human vision behavior and to imitate the inference mechanism of human being. Great quantities of approaches, from visual feature to analysis method, are proposed on this researching path. As we can see, numerous algorithms have been presented on some particular applications, and have been proved to be effective. However, common solutions or uniform frameworks for most problems are still not available, due to the complicated circumstance of real world and the changeability of tracked object itself. Some crucial challenges need to be settled, such as background noises, illumination changes, inter-object occlusions, object's appearance and pose changes.
After thoroughly studied on the art-of-state object tracking algorithms, this dissertation intensively investigated applications of Generalized Hough Transform in object tracking field, discussed some key issues on this topic and proposed a series of effective solutions, including classifier design, visual feature selection, online model updating, location estimation, occlusion reasoning, and multi-object association, etc. The major achievements include:
(1) Semi-supervised Hough Forest Tracking Method. Proposed a flexible semi-supervised learning method based on Hough forests classifier. Introduced a random label distribution, which is based on spatial consistency in context and object-specific information detected in tracking procedure, to improve the performance of classifier, and reduce confusion between inner-class objects. Meanwhile, a Particle-Filtering-Kind random sampling scheme was implemented in detection and updating phases, which can accelerate the whole tracking procedure.
(2) Detection feedback based long-term tracking algorithm. Introduced close-loop idea into tracking field. Used optic-flow based tracker as the feedforward controller, and a detector based on complementary features as feedback controller. Then the latter one's result was used to adjust the tracker's parameters and to maintain the appearance model of the tracked object. This feedback scheme dramatically increased the robustness and stability of tracking system on long-time axis.
(3) Multiple objects tracking with Dual-level Particle Filter Embedded Semi-supervised Hough Forests. Used a dual-level particle filter, which disposed patch random sampling and position random sampling processes in one uniform sampling framework, raised time performance of the detector. And an objects maintenance scheme was designed out to cope with occlusion, background changing, entry/exit and so on.
(4) Multi-pedestrian real-time tracking with multi-view multi-part Hough Forests Detector and on-line Conditional Random Field(CRF) model. Divided pedestrian object into multiple parts, learned full body appearance models and parts appearance models respectively based on online Hough Forests algorithm, which improve the model's robustness and stability against occlusion. And a CRF model was employed to formulate dependencies and affinities between tracklets and detection responses in a short time sliding window to represent current global association, which could handle occlusion and confusion in complex scences well.
引文
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