复杂动态场景中运动目标检测与跟踪算法研究
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摘要
近年来由于严峻的公共安全形势,智能视频监控作为安防领域中最核心的技术之一,得到了广泛应用。智能视频监控的目标是利用信号处理、图像处理、模式识别、机器学习、计算机视觉、人工智能,数据挖掘以及多媒体检索等学科的技术,对采集到的视频进行自动地分析处理,发现监控场景中存在的潜在危险、违规行为或者可疑目标,并对这些行为和目标进行实时报警、提前预警、存储以及事后检索。其研究内容从底层至高层包括:图像预处理、背景建模、运动目标检测、目标跟踪,物体识别和分类、场景语义理解、行为分析以及大范围场景监控中多摄像机之间的联动等。其中运动目标检测与跟踪是智能视频监控系统中的两个核心部分,是实现智能视频监控应用的基础,因而得到了研究者的广泛关注并且积累了大量的研究成果,但实践表明复杂动态场景中的运动目标检测与跟踪算法还远未成熟。特别是真实监控场景中运动目标的快速检测、物体的姿态、尺度、表观和光照的变化,非刚性运动、物体自遮挡和物体之间互遮挡的处理,动态场景(云、烟、雨、雾、阴影、波浪、喷泉、随风摆动的树叶和摄像机的抖动)等给运动目标检测与跟踪的研究带来了极大的挑战。本文针对这些问题进行了研究,包括动态场景中的运动目标检测与分割、基于分块表观模型和局部背景估计的自适应视觉目标跟踪、以及从多个不是很完美的预言中进行弱监督学习的视觉目标跟踪等。本文主要工作和贡献如下:
1.研究了动态场景中的背景建模与运动目标检测。动态场景中存在的挑战主要包括:随风摆动的树叶、波浪、阴影、光照变化、摄像机抖动、云、烟、雨和雾灯。通过对动态场景的数据进行分析,我们发现动态场景中相邻像素之间存在着空间域上的关联性,即一种共生关系,如果能够有效地描述这种共生关系或对其进行建模,我们将可以实现鲁棒有效的背景建模和运动目标检测。因此,本文研究了如何对动态场景中相邻像素之间的共生关系进行建模,提出了基于纹理和运动模式融合的运动目标检测算法、基于标准差特征的运动目标检测算法、以及基于局部前景/背景标记直方图的运动目标检测算法。实验结果验证了所提出算法,在显示考虑动态场景中相邻像素之间共生关系的情况下,能够实现鲁棒有效的背景建模和运动目标检测。
2.提出了一种动态场景中,基于背景剪除驱动种子选择的运动目标的自动精细分割方法。该方法首先应用一种新的背景剪除方法作为注意选择机制,所提出的背景剪除方法融合了基于像素和基于图像块的背景建模方法的互补特性,因而能够在保证生成尽可能少虚警数和漏检数的前提下,生成尽可能多的前景像素,然后提取前景像素的连通区域。为了得到更为精细的分割目标,我们进一步根据一定的启发式规则,将连通区域和及其周围邻域内的像素分为前景种子像素、背景种子像素和未标记像素。之后使用基于封闭形式的抠图算法,对包含运动目标的连通区域及其周围邻域进行更为精细的分割,这是一种自上而下的启发式抠图和分割方法。实验结果表明,本文所提出的方法能够产生较好的分割效果及较快的分割速度。
3.提出了一种基于分块表观模型和局部背景估计的自适应视觉目标跟踪算法。近年来,为了在复杂动态环境下,实现鲁棒的视觉跟踪,在线构建自适应的目标表观模型引起来越来越多学者的关注。然而,基于自适应表观模型的跟踪方法,有一个固有的“漂移”问题,也就是说在在线更新目标表观的过程,由于跟踪累积误差造成了目标模型会最终脱离真正要跟踪的物体。为了缓解视觉跟踪中的“漂移”问题,同时为了有效地对目标进行遮挡和干扰判断,我们提出了基于分块表观模型和局部背景估计的自适应视觉目标跟踪算法,该算法使用分块表观模型来对前景目标进行建模,同时对前景目标周围的背景进行估计。分块表观模型使得所提出的算法具有了遮挡处理能力,局部背景估计为我们更新目标的表观模型,提供了监督信息,因此能够有效地缓解“漂移”问题。此外,通过使用积分图技术和自适应Bin的核密度估计技术,我们可以快速地计算目标的表观模型和局部背景模型。实验结果表明,相比于其他基于分块表观模型的跟踪算法和基于在线学习的跟踪算法,本文所提出的算法在公开的测试序列上取得了更好的跟踪结果。
4.研究了复杂监控场景下目标的长时间持续跟踪问题,提出了一种从多个不是很完美的预言中进行弱监督学习的视觉目标跟踪通用框架。在该跟踪框架中,我们将视觉跟踪视为一种新的从多个标记源进行联合学习的弱监督学习问题,使用一种概率算法无缝地融合了多个不是很完美的预言者所做出的预言(即:多个具有互补特性的跟踪算法给出的跟踪结果),最终取得了鲁棒有效的跟踪效果。与无监督学习、有监督学习、半监督学习以及直推学习不同,在这种新的从多个标记源进行联合学习的弱监督学习问题中,每个训练样本从多个标记精度(即专家程度)未知的标记源得到多个候选的标记,同时每个训练样本真实的标记也是未知的。学习算法根据这些输入数据,需要同时学习和推论出分类器,每个训练样本的真实类别标记,每个训练样本的标记难度以及每个标记源的标记精度。所提出的跟踪算法,相比其他跟踪算法,具有如下优点: 1)我们使用一种自然的算法无缝地融合了多个具有互补特性的跟踪算法,有效地避免了使用单一跟踪算法的缺陷,最终取得了鲁棒有效的跟踪效果。2)所提出的算法,能够同时对目标的位置和每个跟踪器的精度进行联合概率推论,有效地缓解了跟踪算法的“漂移”问题。3)如何自动和在线评价跟踪算法的精度,是跟踪系统的一个研究热点和难点问题,本文所提出的算法能够在无法事先得到目标真实位置的跟踪问题中,在线评价跟踪算法的精度。4)本文所提出的跟踪算法,能够处理缺失标记的问题,也就是说每一个不是很完美的预言者不需要标记所有的训练样本。5)本文提出了一种可扩展和灵活定制的跟踪框架,在我们的跟踪框架中,每一个不是很完美的预言者不仅可以是任何一种通用的跟踪算法,也可以是任何一种鲁棒的或只针对某类跟踪场景有效的跟踪算法。
In recent years intelligent video surveillance technique is popularly used due to serious public safety situation. The goal of intelligent video surveillance technique is to automatically analyze the captured videos for finding potential danger, illegal behaviors or suspicious targets in the scenes. Thus, the technique can provide real-time alarm, early warning, storage and later retrieval. This intelligent video surveillance task underlies several subfields, such as signal processing, image processing, pattern recognition, machine learning, computer vision, artificial intelligence, data mining and multi-media retrieval. From low level to high level, the research content of an intelligent video surveillance system mainly comprises the following key components: image preprocessing, background modeling, moving object detection, visual tracking, object recognition and classification, semantic scene understanding, behavior analysis and continuously tracking multiple objects between multiple cameras. Among these components, the most two basic algorithms are moving object detection and tracking and they have attracted significant attention in the literature. However, practical experience has shown that moving object detection and tracking technologies are currently far from mature. A great number of challenges need to be solved before one can implement a robust visual tracking system for commercial applications, such as fast moving object detection, pose variations, scale variations, appearance variations of the object, illumination changes, non-rigid shape variations, occlusions, cluttered scenes, dynamic scenes, etc. This dissertation proposes several algorithms to addressing these problems, such as moving object detection and segmentation in dynamic scenes, adaptive tracking via patch-based appearance model and local background estimation, and visual tracking via weakly supervised learning from multiple imperfect oracles. The main work and contributions of this thesis are as follows.
Firstly, moving object detection in dynamic scenes is addressed. The challenges in dynamic scenes include trees waving, water rippling, moving shadow, illumination changes, camera jitters, cloud, smoke, fog, rain, etc. After analyzing the video data from dynamic scenes, we found that neighboring pixels tend to be similarly affected by environmental effects (e.g., dynamic scenes.) and can explicitly utilize the correlation of image variations (i.e., co-occurrence statistics) at neighboring pixels to achieve robust detection performance. Thus, we systematically study how to modeling the co-occurrence statistics at neighboring pixels in dynamic scenes and propose three algorithms, such as, texture and motion pattern fusion for moving object detection, standard variance feature for moving object detection, and local histogram of figure/ground segmentation for moving object detection. Experimental results verify that the proposed algorithms can achieve robust and effective moving object detection when they explicitly utilize the co-occurrence statistics at neighboring pixels.
Secondly, we propose a background subtraction driven seeds selection method for moving object segmentation. Specifically, the proposed method can be divided into three main steps. First, we use a novel BGS method as attention mechanisms, generating many possible foreground pixels by tuning it for low false-positives and false-negatives as much as possible. Second, a connected components algorithm is used to give bounding boxes of the labeled foreground pixels. Finally, matting of the object associated to a given bounding box is performed using a heuristic seeds selection scheme. This segmentation and matting task is guided by top-down knowledge. Experimental results demonstrate the efficiency and effectiveness of the proposed method.
Thirdly, we propose a robust visual tracking algorithm via a patch-based adaptive appearance model driven by local background estimation. Long-term persistent tracking in ever-changing environments is a challenging task, which often requires addressing difficult object appearance update problems. To solve them, most top-performing methods rely on online learning-based algorithms. Unfortunately, one inherent problem of online learning-based trackers is drift, a gradual adaptation of the tracker to non-targets. To simultaneously address the tracker drift and occlusion problem, we propose a robust visual tracking algorithm via a patch-based adaptive appearance model driven by local background estimation. First, an object is represented with a patch-based appearance model, in which each patch outputs a confidence map during the tracking. Then, these confidence maps are combined via a robust estimator to finally get more robust and accurate tracking results. Moreover, we present a local spatial co-occurrence based background modeling approach to automatically estimate the local context back-ground model of an interested object captured from a single camera, which may be stationary or moving. Finally, we utilize local background estimation to provide supervision to an analysis of possible occlusions and the adaption of patch-based appearance model of an object. Qualitative and quantitative experimental results on challenging videos demonstrate the robustness of the proposed method.
Fourthly, we systematically study long-term persistent tracking in ever-changing environments and propose a general tracking framework via weakly supervised learning from multiple imperfect oracles. Within this framework, we consider visual tracking in a novel weakly supervised learning scenario where (possibly noisy) labels but no ground truth is provided by multiple imperfect oracles (i.e., trackers), some of which may be mediocre. The problem of learning from multiple labeling sources is different from the unsupervised, supervised, semi-supervised or transductive learning problems, in which each training instance is given a set of candidate class labels provided by different labelers with varying accuracy and the ground truth label of each instance is unknown. Without the ground truth, how to learn classifiers, evaluate the labelers, infer the ground truth label of each data point, and estimate the difficulty of each data point are the main issues addressed by the task of learning from multiple labeling sources. Our method has the following advantages: (1) We propose a natural way of fusing multiple imperfect oracles to get a final reliable and accurate tracking result. The imperfect oracles can be arbitrary tracking algorithms in the literature. This avoids the pitfalls of purely single tracking approaches.(2) The proposed algorithm gives an estimate of the ground truth labeling of training data during tracking in a robust probabilistic inference manner and thus can alleviate the tracker drift problem.(3) We can online evaluate tracking algorithms in the absence of ground truth, which is an important and challenging problem in visual tracking systems.(4) The proposed approach can also handle missing labels (i.e., each tracker is not required to label all the image patches).(5) We propose a scalable and off the shelf tracking framework, in which the imperfect oracles are not necessarily to be primitive trackers, but may be rather powerful and, perhaps problem specific trackers.
1.研究了动态场景中的背景建模与运动目标检测。动态场景中存在的挑战主要包括:随风摆动的树叶、波浪、阴影、光照变化、摄像机抖动、云、烟、雨和雾灯。通过对动态场景的数据进行分析,我们发现动态场景中相邻像素之间存在着空间域上的关联性,即一种共生关系,如果能够有效地描述这种共生关系或对其进行建模,我们将可以实现鲁棒有效的背景建模和运动目标检测。因此,本文研究了如何对动态场景中相邻像素之间的共生关系进行建模,提出了基于纹理和运动模式融合的运动目标检测算法、基于标准差特征的运动目标检测算法、以及基于局部前景/背景标记直方图的运动目标检测算法。实验结果验证了所提出算法,在显示考虑动态场景中相邻像素之间共生关系的情况下,能够实现鲁棒有效的背景建模和运动目标检测。
2.提出了一种动态场景中,基于背景剪除驱动种子选择的运动目标的自动精细分割方法。该方法首先应用一种新的背景剪除方法作为注意选择机制,所提出的背景剪除方法融合了基于像素和基于图像块的背景建模方法的互补特性,因而能够在保证生成尽可能少虚警数和漏检数的前提下,生成尽可能多的前景像素,然后提取前景像素的连通区域。为了得到更为精细的分割目标,我们进一步根据一定的启发式规则,将连通区域和及其周围邻域内的像素分为前景种子像素、背景种子像素和未标记像素。之后使用基于封闭形式的抠图算法,对包含运动目标的连通区域及其周围邻域进行更为精细的分割,这是一种自上而下的启发式抠图和分割方法。实验结果表明,本文所提出的方法能够产生较好的分割效果及较快的分割速度。
3.提出了一种基于分块表观模型和局部背景估计的自适应视觉目标跟踪算法。近年来,为了在复杂动态环境下,实现鲁棒的视觉跟踪,在线构建自适应的目标表观模型引起来越来越多学者的关注。然而,基于自适应表观模型的跟踪方法,有一个固有的“漂移”问题,也就是说在在线更新目标表观的过程,由于跟踪累积误差造成了目标模型会最终脱离真正要跟踪的物体。为了缓解视觉跟踪中的“漂移”问题,同时为了有效地对目标进行遮挡和干扰判断,我们提出了基于分块表观模型和局部背景估计的自适应视觉目标跟踪算法,该算法使用分块表观模型来对前景目标进行建模,同时对前景目标周围的背景进行估计。分块表观模型使得所提出的算法具有了遮挡处理能力,局部背景估计为我们更新目标的表观模型,提供了监督信息,因此能够有效地缓解“漂移”问题。此外,通过使用积分图技术和自适应Bin的核密度估计技术,我们可以快速地计算目标的表观模型和局部背景模型。实验结果表明,相比于其他基于分块表观模型的跟踪算法和基于在线学习的跟踪算法,本文所提出的算法在公开的测试序列上取得了更好的跟踪结果。
4.研究了复杂监控场景下目标的长时间持续跟踪问题,提出了一种从多个不是很完美的预言中进行弱监督学习的视觉目标跟踪通用框架。在该跟踪框架中,我们将视觉跟踪视为一种新的从多个标记源进行联合学习的弱监督学习问题,使用一种概率算法无缝地融合了多个不是很完美的预言者所做出的预言(即:多个具有互补特性的跟踪算法给出的跟踪结果),最终取得了鲁棒有效的跟踪效果。与无监督学习、有监督学习、半监督学习以及直推学习不同,在这种新的从多个标记源进行联合学习的弱监督学习问题中,每个训练样本从多个标记精度(即专家程度)未知的标记源得到多个候选的标记,同时每个训练样本真实的标记也是未知的。学习算法根据这些输入数据,需要同时学习和推论出分类器,每个训练样本的真实类别标记,每个训练样本的标记难度以及每个标记源的标记精度。所提出的跟踪算法,相比其他跟踪算法,具有如下优点: 1)我们使用一种自然的算法无缝地融合了多个具有互补特性的跟踪算法,有效地避免了使用单一跟踪算法的缺陷,最终取得了鲁棒有效的跟踪效果。2)所提出的算法,能够同时对目标的位置和每个跟踪器的精度进行联合概率推论,有效地缓解了跟踪算法的“漂移”问题。3)如何自动和在线评价跟踪算法的精度,是跟踪系统的一个研究热点和难点问题,本文所提出的算法能够在无法事先得到目标真实位置的跟踪问题中,在线评价跟踪算法的精度。4)本文所提出的跟踪算法,能够处理缺失标记的问题,也就是说每一个不是很完美的预言者不需要标记所有的训练样本。5)本文提出了一种可扩展和灵活定制的跟踪框架,在我们的跟踪框架中,每一个不是很完美的预言者不仅可以是任何一种通用的跟踪算法,也可以是任何一种鲁棒的或只针对某类跟踪场景有效的跟踪算法。
In recent years intelligent video surveillance technique is popularly used due to serious public safety situation. The goal of intelligent video surveillance technique is to automatically analyze the captured videos for finding potential danger, illegal behaviors or suspicious targets in the scenes. Thus, the technique can provide real-time alarm, early warning, storage and later retrieval. This intelligent video surveillance task underlies several subfields, such as signal processing, image processing, pattern recognition, machine learning, computer vision, artificial intelligence, data mining and multi-media retrieval. From low level to high level, the research content of an intelligent video surveillance system mainly comprises the following key components: image preprocessing, background modeling, moving object detection, visual tracking, object recognition and classification, semantic scene understanding, behavior analysis and continuously tracking multiple objects between multiple cameras. Among these components, the most two basic algorithms are moving object detection and tracking and they have attracted significant attention in the literature. However, practical experience has shown that moving object detection and tracking technologies are currently far from mature. A great number of challenges need to be solved before one can implement a robust visual tracking system for commercial applications, such as fast moving object detection, pose variations, scale variations, appearance variations of the object, illumination changes, non-rigid shape variations, occlusions, cluttered scenes, dynamic scenes, etc. This dissertation proposes several algorithms to addressing these problems, such as moving object detection and segmentation in dynamic scenes, adaptive tracking via patch-based appearance model and local background estimation, and visual tracking via weakly supervised learning from multiple imperfect oracles. The main work and contributions of this thesis are as follows.
Firstly, moving object detection in dynamic scenes is addressed. The challenges in dynamic scenes include trees waving, water rippling, moving shadow, illumination changes, camera jitters, cloud, smoke, fog, rain, etc. After analyzing the video data from dynamic scenes, we found that neighboring pixels tend to be similarly affected by environmental effects (e.g., dynamic scenes.) and can explicitly utilize the correlation of image variations (i.e., co-occurrence statistics) at neighboring pixels to achieve robust detection performance. Thus, we systematically study how to modeling the co-occurrence statistics at neighboring pixels in dynamic scenes and propose three algorithms, such as, texture and motion pattern fusion for moving object detection, standard variance feature for moving object detection, and local histogram of figure/ground segmentation for moving object detection. Experimental results verify that the proposed algorithms can achieve robust and effective moving object detection when they explicitly utilize the co-occurrence statistics at neighboring pixels.
Secondly, we propose a background subtraction driven seeds selection method for moving object segmentation. Specifically, the proposed method can be divided into three main steps. First, we use a novel BGS method as attention mechanisms, generating many possible foreground pixels by tuning it for low false-positives and false-negatives as much as possible. Second, a connected components algorithm is used to give bounding boxes of the labeled foreground pixels. Finally, matting of the object associated to a given bounding box is performed using a heuristic seeds selection scheme. This segmentation and matting task is guided by top-down knowledge. Experimental results demonstrate the efficiency and effectiveness of the proposed method.
Thirdly, we propose a robust visual tracking algorithm via a patch-based adaptive appearance model driven by local background estimation. Long-term persistent tracking in ever-changing environments is a challenging task, which often requires addressing difficult object appearance update problems. To solve them, most top-performing methods rely on online learning-based algorithms. Unfortunately, one inherent problem of online learning-based trackers is drift, a gradual adaptation of the tracker to non-targets. To simultaneously address the tracker drift and occlusion problem, we propose a robust visual tracking algorithm via a patch-based adaptive appearance model driven by local background estimation. First, an object is represented with a patch-based appearance model, in which each patch outputs a confidence map during the tracking. Then, these confidence maps are combined via a robust estimator to finally get more robust and accurate tracking results. Moreover, we present a local spatial co-occurrence based background modeling approach to automatically estimate the local context back-ground model of an interested object captured from a single camera, which may be stationary or moving. Finally, we utilize local background estimation to provide supervision to an analysis of possible occlusions and the adaption of patch-based appearance model of an object. Qualitative and quantitative experimental results on challenging videos demonstrate the robustness of the proposed method.
Fourthly, we systematically study long-term persistent tracking in ever-changing environments and propose a general tracking framework via weakly supervised learning from multiple imperfect oracles. Within this framework, we consider visual tracking in a novel weakly supervised learning scenario where (possibly noisy) labels but no ground truth is provided by multiple imperfect oracles (i.e., trackers), some of which may be mediocre. The problem of learning from multiple labeling sources is different from the unsupervised, supervised, semi-supervised or transductive learning problems, in which each training instance is given a set of candidate class labels provided by different labelers with varying accuracy and the ground truth label of each instance is unknown. Without the ground truth, how to learn classifiers, evaluate the labelers, infer the ground truth label of each data point, and estimate the difficulty of each data point are the main issues addressed by the task of learning from multiple labeling sources. Our method has the following advantages: (1) We propose a natural way of fusing multiple imperfect oracles to get a final reliable and accurate tracking result. The imperfect oracles can be arbitrary tracking algorithms in the literature. This avoids the pitfalls of purely single tracking approaches.(2) The proposed algorithm gives an estimate of the ground truth labeling of training data during tracking in a robust probabilistic inference manner and thus can alleviate the tracker drift problem.(3) We can online evaluate tracking algorithms in the absence of ground truth, which is an important and challenging problem in visual tracking systems.(4) The proposed approach can also handle missing labels (i.e., each tracker is not required to label all the image patches).(5) We propose a scalable and off the shelf tracking framework, in which the imperfect oracles are not necessarily to be primitive trackers, but may be rather powerful and, perhaps problem specific trackers.
引文
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