智能视频监控的运动目标分类技术研究

发布时间：2019-01-29 01:35

【摘要】：社会在不断的发展，经济与科技的进步也越来越大，人们对工作和生活中的安全问题的重视程度越来越高，视频监控设备也随之大大普及，现在，无论你在任何公共的场合，基本上都在视频监控范围内。视频监控的普及带来的数据量是巨大的，像过去那样找专门的安保工作人员来24小时监视监控视频数据变得不可能，这使得现在的监控视频更多只是作为事故发生后用于侦察破案的依据，而不能作为预防犯罪事故发生的主要手段。因此，智能视频监控技术得到了极大的重视，让计算机代替人去监视监控设备的需求越来越大。在智能视频监控技术中，对运动目标的分类技术先得尤为重要。智能视频监控系统的最终目标就是理解视频中特定目标的行为，从而发现潜在的不安全因素，向外界发出警报。然而，在监控视频中存在着大量的运动目标，如行人、车辆、树叶、噪声等，要理解我们感兴趣的运动目标的行为，首先要做的就是确定这些运动的目标的类型。本文主要针对拍摄距离较远运动目标形状相对较小的目标分类问题展开研究。首先，介绍了智能视频监控中运动目标分类相关的一些技术，综合使用这些技术，提出了一种针对运动目标相对较小、清晰度不高的运动目标分类方法，并使用这种方法，对真实的场景进行应用，把运动目标分为人、人群、车辆和其他四个类别。本文的工作可归纳如下： 1.针对本文所提出的分类算法所使用相关算法以及智能视频监控技术所涉及的常用算法进行介绍，，重点介绍了运动目标分类技术，以基于运动特征和基于静态特征两个方面对运动目标分类技术进行阐述，也重点介绍了支持向量机分类器。 2.针对中远距离拍摄情况下，对外形较小、清晰度不高的运动目标提出了一种基于轮廓HOG的特征包分类方法，并描述了如何在轮廓图像上对HOG特征进行快速运算，使用特征包技术进行降维形成最最终的特征向量。 3.对本文提出的分类方法进行实验分析，得到最优的参数组合，其他文献中的分类算法进行对比，证明本分类方法的可行性。
[Abstract]:With the development of society, the progress of economy and science and technology, people pay more and more attention to the security problems in their work and life, and video surveillance equipment becomes more and more popular. Now, no matter you are in any public place, Basically in the video surveillance range. The amount of data generated by the popularity of video surveillance is so large that it is impossible to find specialized security personnel to monitor video data 24 hours a day, as in the past. This makes the current surveillance video more as the basis of detection and detection after the accident, but not as the main means to prevent crime accidents. Therefore, intelligent video surveillance technology has received great attention. In the intelligent video surveillance technology, the classification of moving targets is very important. The ultimate goal of an intelligent video surveillance system is to understand the behavior of a specific target in a video, thereby detecting potential unsafe factors and alerting the outside world. However, there are a large number of moving targets in surveillance video, such as pedestrians, vehicles, leaves, noise and so on. In order to understand the behavior of moving targets of interest to us, the first thing to do is to determine the types of moving targets. This paper focuses on the classification of objects with relatively small shape of moving objects. Firstly, this paper introduces some technologies related to moving object classification in intelligent video surveillance. By using these technologies, a new method of moving target classification is proposed, which is aimed at the relatively small moving target and low definition, and uses this method. The real scene is used to classify the moving targets into people, crowds, vehicles and other four categories. The work of this paper can be summarized as follows: 1. The related algorithms used in the classification algorithm and the common algorithms involved in the intelligent video surveillance technology are introduced in this paper, and the moving target classification technology is emphasized. Based on motion feature and static feature, the technology of moving target classification is discussed, and the support vector machine classifier is also introduced. 2. In this paper, a feature packet classification method based on contour HOG is proposed for moving objects with small shape and low definition in the case of medium and long range shooting, and how to quickly calculate HOG features on contour images is described. The feature packet technique is used to reduce the dimension to form the final feature vector. 3. Through the experimental analysis of the classification method proposed in this paper, the optimal parameter combination is obtained, and the classification algorithms in other literatures are compared to prove the feasibility of this classification method.
【学位授予单位】：华南理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN948.6

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