基于视频的目标检测、跟踪及其行为识别研究
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摘要
目标的检测、跟踪与行为识别作为视频监控的主要研究内容,是当前计算机视觉领域的研究热点,其不但具有重要的实际意义,而且对计算机视觉的其他研究领域有着重要的推动作用。视频监控技术研究的主要目的是赋予机器视觉系统人类的视觉感知功能,以能够在图像序列中发现目标、跟踪目标,并对目标的行为进行识别和理解。经过几十年尤其是近十年的不懈研究,上述技术取得了长足的进步,但实践表明一般意义上的目标检测、跟踪与行为识别技术还远未成熟,要开发出真正鲁棒、实用的视频监控系统还需要更为鲁棒的核心算法。本论文主要对视频监控相关的关键技术进行研究,研究内容涉及目标的检测、目标的跟踪以及人体行为识别等方向,内容涵盖了可见光图像和红外图像领域。现对论文的主要创新点概述如下:
1)提出一种基于几何约束和颜色信息的人脸检测算法
该方法在充分考虑人脸区域与头发区域的颜色特征与几何关系的基础上,给出了用于描述人脸区域和头发区域之间几何约束的表达模型;在对肤色区域和头发区域分别进行检测后,根据不同候选区域之间的几何关系,通过几何约束对人脸和头发可能存在的区域进行特征判别,完成对图像中的人脸检测。
2)分别提出一种单幅红外图像中和序列红外图像中的人体检测方法
在单幅红外图像中,针对红外图像中人体图像亮度较高的特点,首先通过亮度方向投影确定可能存在人体的候选区域,进而采用方向梯度直方图对候选目标进行描述。最后将方向梯度直方图作为输入向量采用Fisher线性判别和贝叶斯分类器对候选目标进行分类,完成对候选目标中存在的人体进行检测。在红外序列图像中,首先采用自适应高斯混合模型对序列图像进行背景建模,在准确分割出前景目标的基础上,设计了一种新的人体目标表达模型。以人体表达模型作为输入向量,构建支持向量机对人体目标进行分类判别。在不同的红外场景下进行人体检测实验时,所提出的两种算法均取得了满意的检测效果。
3)从自适应目标表达特征的选取和遮挡情况下的目标跟踪两个角度出发对Mean-Shift框架下的目标跟踪问题进行了改进
Mean-Shift框架下的目标跟踪大多采用静态目标表达模型,这在动态变化的场景中容易导致跟踪失败。针对该缺点,论文提出一种基于自适应特征生成模型的目标跟踪方法。通过构建目标和背景的局部信噪比,对当前目标所处特征空间的可跟踪性进行量化评估,选用性能最优的表达模型作为当前的特征跟踪模型。实验表明,与采用静态模型相比,提出的算法具有更好的鲁棒性和可行件。
经典的Mean-Shift算法要求目标在连续两帧之间部分的重合,在目标发生遮挡时难以满足该条件。该论文将目标的运动在较短时间内看作一时不变系统,通过引入Kalman滤波进行参数辨识而使发生遮挡后的跟踪系统具有后续状态预测的能力。整个跟踪过程分为Mean-Shift跟踪下的Kalman参数辨识和基于Kalman状态估计的Bhattacharyya系数分析两个子过程交替执行。对不同的视频序列测试的结果表明,算法能够对发生遮挡后的目标进行持续、稳健的跟踪。
4)在粒子滤波跟踪框架下提出一种多线索融合方法
在复杂的动态背景下,采用多线索进行目标跟踪可以提高系统的鲁棒性。论文在注意到不同特征具有不同的鉴别性能的基础上,从分析采样粒子和参考样本的特征空间距离和物理距离之间的关系出发,提出相对鉴别系数这一概念对不同特征问的鉴别性能进行描述,进而采用二次加权的方法对不同特征进行融合。实验结果表明,所提出的算法在多个复杂场景下均能够对目标进行准确、鲁棒的跟踪。
5)提出一种基于周期运动分析的人体运动识别方法
人体运动识别是视频监控的最终研究目标之一,论文对此提出一种基于周期运动分析的人体运动识别方法。该方法首先通过背景抽取获取人体的前景图像,并采用3个参数对人体的轮廓变化进行测量,以获取人体运动的周期。然后将人体图像投影到一个低维的特征空间中,此时同一周期所包含的特征点组成一个封闭的环。最后设计了一种特征距离函数求取不同的运动环之间的距离,对不同的运动进行分类识别。
上述的几个创新点,按照在视频监控任务中所处的层次,由低到高有机连接,为视频监控技术的实用化提供了理论支持。
As the main research tasks in video surveillance, object detection, tracking and behavior recognition are widely studied in computer vision. They not only have great practical significances, but also play important roles to other topics' progress in computer vision. The ultimate goal of object detection, tracking and behavior recognition is to give the machine vision system human's cognitive ability, so as to find, track and recognize behavior of the target in image sequences. After more than tens years' development, great progress has been made in this field especially during the past ten years. However, practical experience has shown that video surveillance technologies are currently far from mature. A great number of challenges need to be solved before one can implement a robust video surveillance system for commercial applications.
This thesis tries to get insights on some key issues in video surveillance; these issues include face detection, human detection in infrared images, object tracking and human behavior classification. Some important technologies and solutions are studied which are necessary for robust and practical video surveillance systems. The main contributions of this thesis can be concluded as follows.
1) A face detection algorithm is proposed based on the color and geometric information. In this method, a geometric model is designed to describe geometric relations between the face region and the hair region. After a coarse detecting for the skin likeness regions and hair likeness regions, the geometric constrains between them is adopted to detect the face and the hair. Different detecting results indicate that the proposed method is both efficient and robust.
2) The pedestrian detection problem in infrared images is solved from two aspects: pedestrian detection in single image and pedestrian detection in image sequences. For pedestrian detection in single infrared image, the regions of interest (ROI) are located based on the high brightness property of the pedestrian pixels, and then histograms of oriented gradients (HOG) are adopted to describe the ROI. Taking HOG as input vector, the pedestrian region is detected through Fisher linear discriminant and Bayesian classifier. For pedestrian detection in infrared image sequences, we first adopted a GMM (Gauss Mixture Model) to construct the background model, and then on the basis of segmenting the forward object, a shape-based human representing vector was designed. Taking account of occlusions among multi-humans, the intensity projection curve was applied to separate single body from occlusions. Finally, we took human shape vectors as samples, training a SVM (Support Vector Machine) classifier to detect the humans among foreground objects. Experimental results show that both of the two proposed methods are robust and efficient.
3) Two algorithms are proposed to improve the performances of Mean-Shift tracking framework. On the one hand, according to the poor tracking ability when adopts static feature model, this thesis presents an adaptive feature generating model based tracking program. In this program, the object is seemed as valid tracking signal, on the contrary, the background is seemed as noise; after constructing the likelihood maps, a local SNR (Signal Noise Ratio) is computed to evaluate the tracking ability of current feature space, and the feature space with maximal SNR is selected as the optimal tracking feature space. On the other hand, an improved Mean-Shift based tracking algorithm is proposed to solve the poor tracking ability problem in occlusions. A time-invariant system is used to describe the movement of the target during a short time sequences, and through Kalman filter we identify this system so as to make it have ability estimate the coming states while occlusions taken place. The whole tracking system can divided into two parts: a Kalman parameter identifying system based on the object tracking and a Bhattacharyya coefficient analyzing system based on the Kalman state estimating; in the tracking process those two parts run by turns according to different cases. Experiment results of variant video sequences demonstrate that the proposed methods are more robust and feasible than the classical one.
4) An algorithm for fusing multiple cues adaptively in particle filter tracking framework is proposed. Though it is noted that the fusion of multiple cues will lead to an increased reliability of the tracking system, most of current tracking algorithms are based on single cue and are, therefore, often limited to a particular environment. This thesis present a novel multi-cue based tracking method under the particle filter framework. Taking account of both the practical distance and the Bhattacharyya distance between particles and target, a parameter which called Relative Discriminant Coefficient (RDC) is presented to measure the tracking ability for different features. Multi-cue fusion is carried out in a reweighing manner based on this parameter. Experimental results demonstrate the high robustness and effectiveness of our method in handling appearance changes, cluttered background, illumination changes and occlusions.
5) A periodic motion analysis based human action recognition algorithm is proposed. Recognizing human action is a critical step in many computer vision applications. In this thesis the human behavior classification problem is addressed from a periodic motion analysis viewpoint. Our approach uses human silhouettes as motion features which can be obtained very efficiently, and subsequently the periodic motions are obtained by measuring the human shape's deformation. After a periodic analysis, each action unit is represented as a closed loop in the projection space and matching is performed to deicide the action's type by computing the distances among these loops. To demonstrate the effectiveness of this approach, human behavior classification experiments were performed on an open dataset. Classification results are very accurate and show that this approach is promising and efficient.Above proposed novel ideas in this thesis are try to solve three basic problems in video surveillance research: object detection, object tracking and object behavior recognition, and provide theoretic basis for commercial applications. These proposed novel ideas are associated with each other according to the research level in video surveillance.
1)提出一种基于几何约束和颜色信息的人脸检测算法
该方法在充分考虑人脸区域与头发区域的颜色特征与几何关系的基础上,给出了用于描述人脸区域和头发区域之间几何约束的表达模型;在对肤色区域和头发区域分别进行检测后,根据不同候选区域之间的几何关系,通过几何约束对人脸和头发可能存在的区域进行特征判别,完成对图像中的人脸检测。
2)分别提出一种单幅红外图像中和序列红外图像中的人体检测方法
在单幅红外图像中,针对红外图像中人体图像亮度较高的特点,首先通过亮度方向投影确定可能存在人体的候选区域,进而采用方向梯度直方图对候选目标进行描述。最后将方向梯度直方图作为输入向量采用Fisher线性判别和贝叶斯分类器对候选目标进行分类,完成对候选目标中存在的人体进行检测。在红外序列图像中,首先采用自适应高斯混合模型对序列图像进行背景建模,在准确分割出前景目标的基础上,设计了一种新的人体目标表达模型。以人体表达模型作为输入向量,构建支持向量机对人体目标进行分类判别。在不同的红外场景下进行人体检测实验时,所提出的两种算法均取得了满意的检测效果。
3)从自适应目标表达特征的选取和遮挡情况下的目标跟踪两个角度出发对Mean-Shift框架下的目标跟踪问题进行了改进
Mean-Shift框架下的目标跟踪大多采用静态目标表达模型,这在动态变化的场景中容易导致跟踪失败。针对该缺点,论文提出一种基于自适应特征生成模型的目标跟踪方法。通过构建目标和背景的局部信噪比,对当前目标所处特征空间的可跟踪性进行量化评估,选用性能最优的表达模型作为当前的特征跟踪模型。实验表明,与采用静态模型相比,提出的算法具有更好的鲁棒性和可行件。
经典的Mean-Shift算法要求目标在连续两帧之间部分的重合,在目标发生遮挡时难以满足该条件。该论文将目标的运动在较短时间内看作一时不变系统,通过引入Kalman滤波进行参数辨识而使发生遮挡后的跟踪系统具有后续状态预测的能力。整个跟踪过程分为Mean-Shift跟踪下的Kalman参数辨识和基于Kalman状态估计的Bhattacharyya系数分析两个子过程交替执行。对不同的视频序列测试的结果表明,算法能够对发生遮挡后的目标进行持续、稳健的跟踪。
4)在粒子滤波跟踪框架下提出一种多线索融合方法
在复杂的动态背景下,采用多线索进行目标跟踪可以提高系统的鲁棒性。论文在注意到不同特征具有不同的鉴别性能的基础上,从分析采样粒子和参考样本的特征空间距离和物理距离之间的关系出发,提出相对鉴别系数这一概念对不同特征问的鉴别性能进行描述,进而采用二次加权的方法对不同特征进行融合。实验结果表明,所提出的算法在多个复杂场景下均能够对目标进行准确、鲁棒的跟踪。
5)提出一种基于周期运动分析的人体运动识别方法
人体运动识别是视频监控的最终研究目标之一,论文对此提出一种基于周期运动分析的人体运动识别方法。该方法首先通过背景抽取获取人体的前景图像,并采用3个参数对人体的轮廓变化进行测量,以获取人体运动的周期。然后将人体图像投影到一个低维的特征空间中,此时同一周期所包含的特征点组成一个封闭的环。最后设计了一种特征距离函数求取不同的运动环之间的距离,对不同的运动进行分类识别。
上述的几个创新点,按照在视频监控任务中所处的层次,由低到高有机连接,为视频监控技术的实用化提供了理论支持。
As the main research tasks in video surveillance, object detection, tracking and behavior recognition are widely studied in computer vision. They not only have great practical significances, but also play important roles to other topics' progress in computer vision. The ultimate goal of object detection, tracking and behavior recognition is to give the machine vision system human's cognitive ability, so as to find, track and recognize behavior of the target in image sequences. After more than tens years' development, great progress has been made in this field especially during the past ten years. However, practical experience has shown that video surveillance technologies are currently far from mature. A great number of challenges need to be solved before one can implement a robust video surveillance system for commercial applications.
This thesis tries to get insights on some key issues in video surveillance; these issues include face detection, human detection in infrared images, object tracking and human behavior classification. Some important technologies and solutions are studied which are necessary for robust and practical video surveillance systems. The main contributions of this thesis can be concluded as follows.
1) A face detection algorithm is proposed based on the color and geometric information. In this method, a geometric model is designed to describe geometric relations between the face region and the hair region. After a coarse detecting for the skin likeness regions and hair likeness regions, the geometric constrains between them is adopted to detect the face and the hair. Different detecting results indicate that the proposed method is both efficient and robust.
2) The pedestrian detection problem in infrared images is solved from two aspects: pedestrian detection in single image and pedestrian detection in image sequences. For pedestrian detection in single infrared image, the regions of interest (ROI) are located based on the high brightness property of the pedestrian pixels, and then histograms of oriented gradients (HOG) are adopted to describe the ROI. Taking HOG as input vector, the pedestrian region is detected through Fisher linear discriminant and Bayesian classifier. For pedestrian detection in infrared image sequences, we first adopted a GMM (Gauss Mixture Model) to construct the background model, and then on the basis of segmenting the forward object, a shape-based human representing vector was designed. Taking account of occlusions among multi-humans, the intensity projection curve was applied to separate single body from occlusions. Finally, we took human shape vectors as samples, training a SVM (Support Vector Machine) classifier to detect the humans among foreground objects. Experimental results show that both of the two proposed methods are robust and efficient.
3) Two algorithms are proposed to improve the performances of Mean-Shift tracking framework. On the one hand, according to the poor tracking ability when adopts static feature model, this thesis presents an adaptive feature generating model based tracking program. In this program, the object is seemed as valid tracking signal, on the contrary, the background is seemed as noise; after constructing the likelihood maps, a local SNR (Signal Noise Ratio) is computed to evaluate the tracking ability of current feature space, and the feature space with maximal SNR is selected as the optimal tracking feature space. On the other hand, an improved Mean-Shift based tracking algorithm is proposed to solve the poor tracking ability problem in occlusions. A time-invariant system is used to describe the movement of the target during a short time sequences, and through Kalman filter we identify this system so as to make it have ability estimate the coming states while occlusions taken place. The whole tracking system can divided into two parts: a Kalman parameter identifying system based on the object tracking and a Bhattacharyya coefficient analyzing system based on the Kalman state estimating; in the tracking process those two parts run by turns according to different cases. Experiment results of variant video sequences demonstrate that the proposed methods are more robust and feasible than the classical one.
4) An algorithm for fusing multiple cues adaptively in particle filter tracking framework is proposed. Though it is noted that the fusion of multiple cues will lead to an increased reliability of the tracking system, most of current tracking algorithms are based on single cue and are, therefore, often limited to a particular environment. This thesis present a novel multi-cue based tracking method under the particle filter framework. Taking account of both the practical distance and the Bhattacharyya distance between particles and target, a parameter which called Relative Discriminant Coefficient (RDC) is presented to measure the tracking ability for different features. Multi-cue fusion is carried out in a reweighing manner based on this parameter. Experimental results demonstrate the high robustness and effectiveness of our method in handling appearance changes, cluttered background, illumination changes and occlusions.
5) A periodic motion analysis based human action recognition algorithm is proposed. Recognizing human action is a critical step in many computer vision applications. In this thesis the human behavior classification problem is addressed from a periodic motion analysis viewpoint. Our approach uses human silhouettes as motion features which can be obtained very efficiently, and subsequently the periodic motions are obtained by measuring the human shape's deformation. After a periodic analysis, each action unit is represented as a closed loop in the projection space and matching is performed to deicide the action's type by computing the distances among these loops. To demonstrate the effectiveness of this approach, human behavior classification experiments were performed on an open dataset. Classification results are very accurate and show that this approach is promising and efficient.Above proposed novel ideas in this thesis are try to solve three basic problems in video surveillance research: object detection, object tracking and object behavior recognition, and provide theoretic basis for commercial applications. These proposed novel ideas are associated with each other according to the research level in video surveillance.
引文
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