基于稀疏表示的图像分类与目标跟踪研究
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摘要
图像分类与目标跟踪是计算机视觉分析领域的分支研究方向，在人机互交、智能交通、无人机制导、智能安防等领域均具有重要的应用价值。稀疏表示(SparseRepresentation)理论是一种新兴的信号表示方法，也是一种对哺乳动物大脑皮层编码机制的模拟，近年来在计算机视觉分析领域得到了较为广泛的关注。此方法因使用超完备字典对信号进行分解，所以对信号的误差与噪声比传统方法更稳健。本文分别从基础理论和应用设计两个方面着手，重点对稀疏表示原理、稀疏表示分类器的设计和基于稀疏表示的图像分类、目标跟踪算法的国内外研究进展进行了调研，并在同行最新研究成果的基础上开展了深入研究。具体研究工作可概括如下：
首先详细介绍了稀疏表示原理的研究意义，认真分析了信号稀疏表示的数学模型、稀疏性的度量方法以及稀疏表示原理在图像分类与目标跟踪中的应用情况。然后，解释了稀疏表示理论的最优解存在性的证明以及求解稀疏表示的贪婪算法与凸松弛方法。对特别是匹配追踪法及其扩展算法进行了详细阐述，另外，介绍了坐标下降法等Lasso问题求解方案，然后，提出了一种核函数非负稀疏表示分类算法以及一种结合判别分析的稀疏分类算法，为稀疏表示的计算机视觉应用奠定了基础。
研究了基于稀疏表示的场景图像分类算法。首先分析了场景图像的特点，对此类任务的特殊难点进行了阐述。然后对常见的场景图像分类算法的方法和框架进行了探索，分析了这些方法的优点与不足。在以上研究的基础上，提出了一种基于多层核函数稀疏分类与多尺度分块旋转扩展的鲁棒图像分类算法。使用多种尺度的网格对训练图像进行分块，对分块图像进行旋转扩展，由此得到的字典能够近似测试图像局部的旋转扭曲与各种排列组合。为了增加字典类间的稀疏度，改善系统效率，提出了一种字典降维策略；然后通过核函数随机坐标下降(KRCD)方法高效求解稀疏分类中的凸优化问题，进而通过比较层稀疏模型中不同类测试图像的重构误差完成图像分类。此方法仅使用少量样本解决场景图像分类中最具挑战的类间相似性与类内多样性问题，与几类经典方法进行比较，此方法在小样本情况下具备更好的识别效果，对图像旋转或局部扭曲变形等复杂情况具有较好的鲁棒性。
对目标跟踪算法进行了研究，深入分析了目标跟踪的难点问题，如：目标和背景之间共享相似特征、目标在运动过程中会产生形变、会被遮挡等复杂问题、目标短暂消失等问题等。对常见的目标跟踪框架即卡尔曼滤波与粒子滤波框架进行了研究，对这两种方法各自的优点和缺点进行了比较。针对经典稀疏分类目标跟踪算法中目标模板的建模和更新方式效率低、跟踪性能不可靠等问题，提出了一种基于时空约束与标准对冲的稀疏表示目标跟踪算法。对时空约束原理、目标基、背景基、时序特征池以及两类基更新机制进行了讨论，并提出了时序循环更新方法解决模版更新问题。最终，解释了在标准对冲算法框架下结合稀疏表示分类器实时地求取目标坐标的方法。在实验的基础上，证明此方法比几种经典目标跟踪算法更可靠。
针对经典目标跟踪算法在光照改变、运动模糊等情况下精度不高，长时间目标跟踪不可避免发生跟踪丢失等问题，提出了一种基于核函数并行稀疏分类与稀疏分类器网格的合作目标跟踪算法。该算法也是在标准对冲框架下，结合使用核函数并行稀疏表示分类方法、自适应字典更新方法以及稀疏分类器网格等技术，保证目标跟踪算法在噪声、遮挡等恶劣环境下进行长时间跟踪的可靠性。由于跟踪算法对算法实时性的要求较高，一般的坐标下降方法求解Lasso问题速度无法满足要求，本文提出使用并行化的核函数随机坐标下降算法来高效求解稀疏系数，充分利用现代计算机多核处理器的功能解决效率问题；与其它基于稀疏表示的目标跟踪算法相似，分类器求取的分类信心值可等价转化为各粒子的代价值。然后，为了避免模板漂移问题，解释了目标字典和背景字典的在线更新方法；为了解决永久性跟踪失败的情况，设计了稀疏分类器网格的方法来粗略检测目标状态，并在目标出现之后重启主跟踪器。实验结果证明，本算法的各部分都能增强跟踪效果，相比其它几种经典目标跟踪算法，本算法具有更好的实时性和可靠性。
Sparse representation is a kind of data representation method which similar tohuman cortex, it is a fundamental theory for many signal processing problems, such ascomputer vision, audio signal analysis, and blind signal processing. Among manyresearch fields, the computer vision is well known for its tight connection to sparserepresentation, and it has drawn a lot of interests of researchers recently.
Image classification and target tracking are branches of computer vision analysis;they have been used to construct many systems such as human-computer interface,intelligent transpotation, UAV navigation, intelligent security and shown greatimportance of practical value. Sparse representation is a newly devoleped signalrepresentation theory, it simulates data representation function of manmal cortex. Inrecent years, it has been wildly used in computer vision analysis applications. Theadvantage of sparse representation theory comparing to common methods for signalprocess is that it is more robust to errors and noises. This thesis mainly focuses on thetheory of sparse representation and its applications such as sparse representationclassifier，image classification and target tracking. Related work of other researchers isintroduced, based on those research results, further investigation are also carried out.
The purpose of researching sparse representation theory is introduced; relatedtheories and applications are summarized; mathematical models of sparse representationfor signals, the methods for measuring sparsity and sparse representation principles forimage classification and target tracking are discussed in this thesis. Then, the existenceof optimal solution of sparse representation is proved; greedy methods and convexrelaxation methods are introduced. Furthermore, coordinate decent for solving Lassoproblems are introduced as well; a kernel non-negative sparse representationclassification method and a discriminative analysis based sparse representation methodare proposed, those two methods could be the fundamental basis for many sparserepresentation problems.
The sparse representation based scene image classification method is an importantpart of this thesis. The specialty of scene images and a number of difficulties areintroduced. Frameworks of common scene classification methods, their merit anddemerit are discussed as well. Based on these discussions, a hierarchical kernel sparserepresentation classification and multi-scale block rotation-extension (HKSRC-MSBRE) based robust image recognition method is proposed to deal with the randompermutations and combinations of local images in image recognition tasks. At first, themulti-scale grids are used to cut the training image into pieces, and thenrotation-extended methods are applied to create a dictionary which is adapted to randompermutations and combinations of local images in test sets. To enhance the sparsity ofthe dictionary and increase the efficiency of the system, a new strategy is proposed toreduce the dimensions of the dictionary; then, a kernel random coordinate descentmethod is proposed to solve the convex optimization problem in the KSRC; at last, themethod for calculating the class label of each image is proposed. The experimentalresults show the proposed method has robust performances when dealing with randompermutations and combinations of local images, and it has outperformed otherstate-of-art image recognition methods.
The sparse representation based target tracking method is an important part of thisthesis as well. In order to fully explore this problem, many tough situations arediscussed: such as target and its background sharing similar patterns, deformations andocclusions. Common target tracking framework such as Kalman filter and Particle filterare studied. By casting the tracking problem as a kind of sparse approximation problem,a novel algorithm based on STC and SRC is proposed for object tracking in complicatedscenes. Space constraint is given to ensure background patterns in the template havesmall weights, time constraint is given to handle the target appearance variances. Sparserepresentation classification method for tracking have been proposed to calculate lossvalues and experimental results showed that time-space constraints and sparserepresentation classification method have better results than standard l1-loss method.Proposed tracking algorithm outperforms better than other state-of-art trackingalgorithms in many difficult situations.
At last, in order to solve tough problems such as varying lighting conditions,motion blurring and tracking failure in long-term tracking, a kernel parallel sparserepresentation classification and sparse classifier girds based cooperative target trackingmethod is proposed in this thesis. This tracking method is under normalhedgeframework, it combines a few novel technics such as kernel parallel sparserepresentation classification, automatic dictionary updating and sparse classifier grids,and those technics ensure the reliabilities for long term tracking. Unlike other computervision applications, tracking is a real-time task, and its algorithm must be highlyefficient. The proposed parallel method could fully utilize the multi-core CPU, thus solve the efficiency problem. Similar to other sparse representation based trackingmethods, classification confidence values equal to loss values; furthermore, in order toavoid drifting problems, the online updating method of target dictionary andbackground dictionary is proposed as well. The sparse classifier grids are designed toroughly detect the target after it reappears and reboot the main tracker. Experimentalresults show each part of proposed method could enhance the tracking performance,comparing to other state-of-art methods, the proposed method offers better real-timeperformance and reliability.
首先详细介绍了稀疏表示原理的研究意义，认真分析了信号稀疏表示的数学模型、稀疏性的度量方法以及稀疏表示原理在图像分类与目标跟踪中的应用情况。然后，解释了稀疏表示理论的最优解存在性的证明以及求解稀疏表示的贪婪算法与凸松弛方法。对特别是匹配追踪法及其扩展算法进行了详细阐述，另外，介绍了坐标下降法等Lasso问题求解方案，然后，提出了一种核函数非负稀疏表示分类算法以及一种结合判别分析的稀疏分类算法，为稀疏表示的计算机视觉应用奠定了基础。
研究了基于稀疏表示的场景图像分类算法。首先分析了场景图像的特点，对此类任务的特殊难点进行了阐述。然后对常见的场景图像分类算法的方法和框架进行了探索，分析了这些方法的优点与不足。在以上研究的基础上，提出了一种基于多层核函数稀疏分类与多尺度分块旋转扩展的鲁棒图像分类算法。使用多种尺度的网格对训练图像进行分块，对分块图像进行旋转扩展，由此得到的字典能够近似测试图像局部的旋转扭曲与各种排列组合。为了增加字典类间的稀疏度，改善系统效率，提出了一种字典降维策略；然后通过核函数随机坐标下降(KRCD)方法高效求解稀疏分类中的凸优化问题，进而通过比较层稀疏模型中不同类测试图像的重构误差完成图像分类。此方法仅使用少量样本解决场景图像分类中最具挑战的类间相似性与类内多样性问题，与几类经典方法进行比较，此方法在小样本情况下具备更好的识别效果，对图像旋转或局部扭曲变形等复杂情况具有较好的鲁棒性。
对目标跟踪算法进行了研究，深入分析了目标跟踪的难点问题，如：目标和背景之间共享相似特征、目标在运动过程中会产生形变、会被遮挡等复杂问题、目标短暂消失等问题等。对常见的目标跟踪框架即卡尔曼滤波与粒子滤波框架进行了研究，对这两种方法各自的优点和缺点进行了比较。针对经典稀疏分类目标跟踪算法中目标模板的建模和更新方式效率低、跟踪性能不可靠等问题，提出了一种基于时空约束与标准对冲的稀疏表示目标跟踪算法。对时空约束原理、目标基、背景基、时序特征池以及两类基更新机制进行了讨论，并提出了时序循环更新方法解决模版更新问题。最终，解释了在标准对冲算法框架下结合稀疏表示分类器实时地求取目标坐标的方法。在实验的基础上，证明此方法比几种经典目标跟踪算法更可靠。
针对经典目标跟踪算法在光照改变、运动模糊等情况下精度不高，长时间目标跟踪不可避免发生跟踪丢失等问题，提出了一种基于核函数并行稀疏分类与稀疏分类器网格的合作目标跟踪算法。该算法也是在标准对冲框架下，结合使用核函数并行稀疏表示分类方法、自适应字典更新方法以及稀疏分类器网格等技术，保证目标跟踪算法在噪声、遮挡等恶劣环境下进行长时间跟踪的可靠性。由于跟踪算法对算法实时性的要求较高，一般的坐标下降方法求解Lasso问题速度无法满足要求，本文提出使用并行化的核函数随机坐标下降算法来高效求解稀疏系数，充分利用现代计算机多核处理器的功能解决效率问题；与其它基于稀疏表示的目标跟踪算法相似，分类器求取的分类信心值可等价转化为各粒子的代价值。然后，为了避免模板漂移问题，解释了目标字典和背景字典的在线更新方法；为了解决永久性跟踪失败的情况，设计了稀疏分类器网格的方法来粗略检测目标状态，并在目标出现之后重启主跟踪器。实验结果证明，本算法的各部分都能增强跟踪效果，相比其它几种经典目标跟踪算法，本算法具有更好的实时性和可靠性。
Sparse representation is a kind of data representation method which similar tohuman cortex, it is a fundamental theory for many signal processing problems, such ascomputer vision, audio signal analysis, and blind signal processing. Among manyresearch fields, the computer vision is well known for its tight connection to sparserepresentation, and it has drawn a lot of interests of researchers recently.
Image classification and target tracking are branches of computer vision analysis;they have been used to construct many systems such as human-computer interface,intelligent transpotation, UAV navigation, intelligent security and shown greatimportance of practical value. Sparse representation is a newly devoleped signalrepresentation theory, it simulates data representation function of manmal cortex. Inrecent years, it has been wildly used in computer vision analysis applications. Theadvantage of sparse representation theory comparing to common methods for signalprocess is that it is more robust to errors and noises. This thesis mainly focuses on thetheory of sparse representation and its applications such as sparse representationclassifier，image classification and target tracking. Related work of other researchers isintroduced, based on those research results, further investigation are also carried out.
The purpose of researching sparse representation theory is introduced; relatedtheories and applications are summarized; mathematical models of sparse representationfor signals, the methods for measuring sparsity and sparse representation principles forimage classification and target tracking are discussed in this thesis. Then, the existenceof optimal solution of sparse representation is proved; greedy methods and convexrelaxation methods are introduced. Furthermore, coordinate decent for solving Lassoproblems are introduced as well; a kernel non-negative sparse representationclassification method and a discriminative analysis based sparse representation methodare proposed, those two methods could be the fundamental basis for many sparserepresentation problems.
The sparse representation based scene image classification method is an importantpart of this thesis. The specialty of scene images and a number of difficulties areintroduced. Frameworks of common scene classification methods, their merit anddemerit are discussed as well. Based on these discussions, a hierarchical kernel sparserepresentation classification and multi-scale block rotation-extension (HKSRC-MSBRE) based robust image recognition method is proposed to deal with the randompermutations and combinations of local images in image recognition tasks. At first, themulti-scale grids are used to cut the training image into pieces, and thenrotation-extended methods are applied to create a dictionary which is adapted to randompermutations and combinations of local images in test sets. To enhance the sparsity ofthe dictionary and increase the efficiency of the system, a new strategy is proposed toreduce the dimensions of the dictionary; then, a kernel random coordinate descentmethod is proposed to solve the convex optimization problem in the KSRC; at last, themethod for calculating the class label of each image is proposed. The experimentalresults show the proposed method has robust performances when dealing with randompermutations and combinations of local images, and it has outperformed otherstate-of-art image recognition methods.
The sparse representation based target tracking method is an important part of thisthesis as well. In order to fully explore this problem, many tough situations arediscussed: such as target and its background sharing similar patterns, deformations andocclusions. Common target tracking framework such as Kalman filter and Particle filterare studied. By casting the tracking problem as a kind of sparse approximation problem,a novel algorithm based on STC and SRC is proposed for object tracking in complicatedscenes. Space constraint is given to ensure background patterns in the template havesmall weights, time constraint is given to handle the target appearance variances. Sparserepresentation classification method for tracking have been proposed to calculate lossvalues and experimental results showed that time-space constraints and sparserepresentation classification method have better results than standard l1-loss method.Proposed tracking algorithm outperforms better than other state-of-art trackingalgorithms in many difficult situations.
At last, in order to solve tough problems such as varying lighting conditions,motion blurring and tracking failure in long-term tracking, a kernel parallel sparserepresentation classification and sparse classifier girds based cooperative target trackingmethod is proposed in this thesis. This tracking method is under normalhedgeframework, it combines a few novel technics such as kernel parallel sparserepresentation classification, automatic dictionary updating and sparse classifier grids,and those technics ensure the reliabilities for long term tracking. Unlike other computervision applications, tracking is a real-time task, and its algorithm must be highlyefficient. The proposed parallel method could fully utilize the multi-core CPU, thus solve the efficiency problem. Similar to other sparse representation based trackingmethods, classification confidence values equal to loss values; furthermore, in order toavoid drifting problems, the online updating method of target dictionary andbackground dictionary is proposed as well. The sparse classifier grids are designed toroughly detect the target after it reappears and reboot the main tracker. Experimentalresults show each part of proposed method could enhance the tracking performance,comparing to other state-of-art methods, the proposed method offers better real-timeperformance and reliability.
引文
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