多摄像机视频监控中运动目标检测与跟踪
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摘要
随着视频监控系统的迅速普及,与之相关的多摄像机智能视频监控技术得到了广泛的研究和应用。特别是监控视频中运动目标检测、目标跟踪和多摄像机协同技术,已经成为计算机视觉领域研究的热点,存在着广泛的应用前景。
本文针对多摄像机视频监控系统中的目标检测与跟踪问题进行研究,最终目标是建立一套实用的多摄像机智能监控系统,研究针对实际应用中面临的问题,重在提高算法的实时性和鲁棒性。研究内容主要包括以下三个方面:
运动目标检测:背景差方法是视频运动目标检测中得到广泛应用的一种方法,在对其进行全面研究的基础上,本文提出了自适应多模快速背景差方法,该方法基于场景中模型分布的不均匀性,相对于混合高斯模型背景差方法,增强了模型的适应性,大大提高了算法的速度;本文针对运动摄像机情况下的目标检测进行了有益的探索,将背景差方法与基于SIFT的运动补偿相结合用于目标检测,通过与传统方法的实验结果对比验证了方法的有效性;针对运动目标检测中存在的阴影和鬼影的问题,本文进行了深入的研究,提出了相应的解决方法,并通过实验对比验证了方法的有效性。
遮挡情况下的目标跟踪:不同外形运动目标的遮挡问题是视频目标跟踪中的难点,本文针对遮挡的检测与遮挡情况下的目标跟踪进行研究,在块特征提取的基础上,利用MBB关系进行遮挡的检测,将目标概率外观模型与CONDENSATION相结合进行目标跟踪,不但可以解决行人间遮挡的问题,而且很好的解决了行人与车辆等不同外形目标跟踪中的遮挡问题。
多摄像机协同:多摄像机协同目标跟踪近年来得到了广泛的关注,但现有的研究多针对较理想的实验环境。本文在研究现有技术的基础上,针对实际应用中的室内复杂遮挡环境,在目标匹配、定位和标识三个环节上提出了一系列新的解决方法:(1)提出了一种基于头部检测和对极关系进行匹配的方法,解决了两摄像机视图中受遮挡的行人目标匹配的问题;(2)提出了一种基于三焦点张量点转移的虚拟顶视图中目标定位的方法,解决了对极点转移中对极线交点退化的问题;(3)针对跟踪中出现大量的轨迹片段的问题,提出了一种基于序贯Bayes检测的轨迹标识方法,解决了噪声环境下目标标识的问题。最后,在研究基础上建立了一个室内遮挡环境下多摄像机协同跟踪标识实验系统,对本文方法进行了验证。
With the high-speed development of video monitoring system, video surveillance in dynamic scenes, especially for humans and vehicles, has become one of the most active research topics in computer vision. It has a wide spectrum of promising applications, including access control in special areas, human identification at a distance, crowd flux statistics and congestion analysis, detection of anomalous behaviors, and interactive surveillance using multiple cameras, etc.
This thesis focuses on the key technologies of multi-camera intelligent video surveillance and gives more attention to the enhancement of real-time and robust performance. The ultimate goal is to develop a practical multi-camera intelligent surveillance system. The main contribution consists of three parts as follows.
Motion detection: Background subtraction is widely used in moving objects detection. After a brief review, an adaptive multi-model fast background subtraction is proposed which can automatically adapt to the arbitrary distribution of background pixel process. Compared with mixture Gaussian model algorithm, the method maintains the merits of multi-model and decrease the algorithm time cost greatly. The thesis also attempts to give a solution of motion detection in moving camera video. Background subtraction and SIFT motion compensation are integrated and give a promising experimental result. The issues of moving shadow and ghost shadow in motion detection are also studied here and given solutions respectively.
Occlusions handling: Occlusions is the most difficult point in video object tracking. In the proposed approach, occlusions are detected by analyzing MBB overlapping feature in consecutive frames, objects are described by probabilistic appearance models and tracked through occlusions under framework of CONDENSATION. The merit of this approach is can handle occlusions between objects of various types, such as pedestrian and vehicle.
Multi-camera collaboration: Collaborative multi-camera tracking is a hot topic in recent years. A novel approach is proposed which aim to object matching, location and identification in a complicated multi-camera scene. In the approach, head detection and epipolar relationship is used to confirm correspondence of observations between different camera views, and trifocal tensor transfer is used to locate objects in virtual top view. The sequential Bayesian method is applied to real-time label trajectory after Kalman+PDA tracking. Compared with existing approaches, the approach doesn’t need camera calibration and coplanar precondition, doesn’t exist the deterioration of epipolar transfer, can label the trajectory segments real-time and accurately. A practical multi-camera experimental system in the lab is introduced finally.
本文针对多摄像机视频监控系统中的目标检测与跟踪问题进行研究,最终目标是建立一套实用的多摄像机智能监控系统,研究针对实际应用中面临的问题,重在提高算法的实时性和鲁棒性。研究内容主要包括以下三个方面:
运动目标检测:背景差方法是视频运动目标检测中得到广泛应用的一种方法,在对其进行全面研究的基础上,本文提出了自适应多模快速背景差方法,该方法基于场景中模型分布的不均匀性,相对于混合高斯模型背景差方法,增强了模型的适应性,大大提高了算法的速度;本文针对运动摄像机情况下的目标检测进行了有益的探索,将背景差方法与基于SIFT的运动补偿相结合用于目标检测,通过与传统方法的实验结果对比验证了方法的有效性;针对运动目标检测中存在的阴影和鬼影的问题,本文进行了深入的研究,提出了相应的解决方法,并通过实验对比验证了方法的有效性。
遮挡情况下的目标跟踪:不同外形运动目标的遮挡问题是视频目标跟踪中的难点,本文针对遮挡的检测与遮挡情况下的目标跟踪进行研究,在块特征提取的基础上,利用MBB关系进行遮挡的检测,将目标概率外观模型与CONDENSATION相结合进行目标跟踪,不但可以解决行人间遮挡的问题,而且很好的解决了行人与车辆等不同外形目标跟踪中的遮挡问题。
多摄像机协同:多摄像机协同目标跟踪近年来得到了广泛的关注,但现有的研究多针对较理想的实验环境。本文在研究现有技术的基础上,针对实际应用中的室内复杂遮挡环境,在目标匹配、定位和标识三个环节上提出了一系列新的解决方法:(1)提出了一种基于头部检测和对极关系进行匹配的方法,解决了两摄像机视图中受遮挡的行人目标匹配的问题;(2)提出了一种基于三焦点张量点转移的虚拟顶视图中目标定位的方法,解决了对极点转移中对极线交点退化的问题;(3)针对跟踪中出现大量的轨迹片段的问题,提出了一种基于序贯Bayes检测的轨迹标识方法,解决了噪声环境下目标标识的问题。最后,在研究基础上建立了一个室内遮挡环境下多摄像机协同跟踪标识实验系统,对本文方法进行了验证。
With the high-speed development of video monitoring system, video surveillance in dynamic scenes, especially for humans and vehicles, has become one of the most active research topics in computer vision. It has a wide spectrum of promising applications, including access control in special areas, human identification at a distance, crowd flux statistics and congestion analysis, detection of anomalous behaviors, and interactive surveillance using multiple cameras, etc.
This thesis focuses on the key technologies of multi-camera intelligent video surveillance and gives more attention to the enhancement of real-time and robust performance. The ultimate goal is to develop a practical multi-camera intelligent surveillance system. The main contribution consists of three parts as follows.
Motion detection: Background subtraction is widely used in moving objects detection. After a brief review, an adaptive multi-model fast background subtraction is proposed which can automatically adapt to the arbitrary distribution of background pixel process. Compared with mixture Gaussian model algorithm, the method maintains the merits of multi-model and decrease the algorithm time cost greatly. The thesis also attempts to give a solution of motion detection in moving camera video. Background subtraction and SIFT motion compensation are integrated and give a promising experimental result. The issues of moving shadow and ghost shadow in motion detection are also studied here and given solutions respectively.
Occlusions handling: Occlusions is the most difficult point in video object tracking. In the proposed approach, occlusions are detected by analyzing MBB overlapping feature in consecutive frames, objects are described by probabilistic appearance models and tracked through occlusions under framework of CONDENSATION. The merit of this approach is can handle occlusions between objects of various types, such as pedestrian and vehicle.
Multi-camera collaboration: Collaborative multi-camera tracking is a hot topic in recent years. A novel approach is proposed which aim to object matching, location and identification in a complicated multi-camera scene. In the approach, head detection and epipolar relationship is used to confirm correspondence of observations between different camera views, and trifocal tensor transfer is used to locate objects in virtual top view. The sequential Bayesian method is applied to real-time label trajectory after Kalman+PDA tracking. Compared with existing approaches, the approach doesn’t need camera calibration and coplanar precondition, doesn’t exist the deterioration of epipolar transfer, can label the trajectory segments real-time and accurately. A practical multi-camera experimental system in the lab is introduced finally.
引文
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