摘要
视频监控系统经过多年的发展,在公共安全方面发挥越来越大的作用。近年来人们对公共安全和交通安全越来越重视,传统的视频监控系统需要工作人员长时间集中精力监控,只能做到“事后取证”,而不能“实时预防”,因此越来越不能满足需求。在现代社会,视频监控技术向智能视频监控系统发展已经迫在眉睫。智能视频监控是计算机视觉和模式识别技术理论在视频监控领域内的应用,其中的关键技术问题就是如何选择和设计合适的特征表达图像的内容,进而有效的理解场景图像中的内容。由于被监控场景的复杂性和特征本身的局限性,只用单一特征难以有效表示图像内容以完成前景背景分类。在很多应用中,只有选择和设计多个特征进行有效的融合,才能取得满意的分类效果。本文主要工作针对运动目标检测和跟踪中的问题,研究运用多特征融合以更有效的完成前景运动目标和背景的分类。
本文的主要研究工作和创新如下:
(1)提出了一种新的光照鲁棒的递归背景学习方法以及一种边缘特征和颜色特征融合的背景建模方法
本文提出了一种基于边缘特征的光照鲁棒的递归背景学习方法,该方法可以有效克服局部光照和摄像头扰动给目标检测带来的影响。本文还对基于颜色特征的背景差模型做了总结和定量的分析比较。虽然基于颜色特征的背景差方法可以得到连通的前景目标区域,但由于颜色对光照敏感,在目标的突然移动和局部光照发生变化的情况下,往往会检测出虚假目标。本文通过颜色特征和边缘特征的融合,有效的消除突然光照变化和背景中长时间不动的目标的突然移动而带来的虚假目标。
(2)提出了一种新的基于AdaBoost的前景/阴影分类的特征融合框架
在运动目标检测中,由于移动阴影和真实运动目标在运动等特征方面有着相同的特性,往往会把移动阴影误检测为前景目标,影响目标的识别性能。本文提出了一种基于AdaBoost的前景/阴影分类的特征融合框架,首先设计多个弱分类器,通过给定样本的学习,得到一个多特征融合的线性组合分类器。试验表明,这种方法比以往采用的单一特征方法在检测率上有较大提高。
(3)提出了一种新的基于区域特征分层的背景建模方法
在固定场景的视频监控中,动态背景给运动目标检测带来了困难。本文提出了基于区域特征和局部特征融合的运动目标检测算法。首先按区域特征对背景分层,将场景分为动态背景层和静态背景层。对于不同背景层,采用不同的背景建模方法。对于静态背景层,采用基于颜色特征的个数较少的混合高斯模型对背景建模;对于动态背景层,将颜色特征和运动特征融合,从而检测出前景目标。通过试验表明,本方法在运行效率和检测率比以往的方法有着较大的提高。
(4)提出了一种新的基于马尔可夫随机场的目标跟踪方法
前景目标的尺度变化、旋转变化和遮挡都给目标跟踪带来了困难。本文将运动目标跟踪问题看作是前景和背景的二值分类问题。首先对每一个像素点分别计算它属于前景颜色分布的概率和背景颜色分布的概率,并考虑像素之间的空间一致性,建立前景背景分割的马尔可夫随机场,从而实现对前景背景的分类,以完成对运动目标的跟踪。试验证明,这种方法可以有效的克服前景目标的尺度变化和旋转变化以及遮挡给目标跟踪带来的困难。
Nowsday public safety and traffic safety have been paid more attention on. Traditional video surveillance system can not afford of the demand for it need more human resource and people can't concentrate these attentions on work in long time. Intelligent video surveillance system become a new research hot spot in computer vision. Intelligent video surveillance is the application of computer vision and pattern recognition in filed of video surveillance. A key issue in intelligent video surveillance is how to understand the context of image effectively. For complex of scene and confine of feature, it is difficult that have good performance using a single feature in pattern classification. In many applications, it can have good performance using fusion of multiple features. The work in this paper is mainly how to classification of foreground and background effectively using fusion of multiple features in the moving object detection and tracking.
The main works and invanations of this paper as follows:
(1) A new recursive background learning model based on edge feature is proposed in this paper and this method is robust for illumination change .This method can eliminate the effect of local illumination change and the tiny swing of video captures , but the shortcoming of this method only can have the sparse represention of the moving object. The background models basing on color feature are compared and quantitative analyzed in this paper. The background model based on color feature can the dense represention of the moving object, but this method is sensitive for illumination change. When object stay long time in background begins to move and local illumination change, the flase object will be detected. A new background model basing on fusion of color and edge features is proposed in this paper and this method can elimate the flase object results for the above reasons effectively.
(2) For cast shadow shares the same characteristices in some features, cast shadow is usually mistook to the moving object. It is difficult of the classification of moving object and cast shadow using a single feature .A new feature selection framework for classification of foreground and shadow is proposed in this paper. By learning using the give samples, a linear classifier of fusion of multiple features is trained. Experierment demonstrates this model has better performance than the model using a single feature.
(3) A new layered background model on fusion of region feature and local feature is proposed in this paper. The background is layered by region feature. The scene is divided to dynamic background layer and static background layer. Different background model is adopted for different layer. A Gaussian Mixture Model based on color feature is adopted in static layer. A background model basing on fusion of motion feature and color feature is adopted in dynamic layer. Experierment demonstrates this model has better performance and less run time than the previous model.
(4) Scale change, rotate change and occluding of moving object can cause to the failure of object tracking. The problem of object tracking is a binary label problem. First the probabilities of pixies belong to forground and background is calcaculted. Cosidering the space coherence of pixels, a markov random filed of foreground segment is modeled. Experierment demonstrates this model is robust for scale change, rotate change and occluding.
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