基于视频监控的教室人数统计

发布时间：2018-04-30 13:43

本文选题：视频监控 + 人数统计　；参考：《安徽大学》2014年硕士论文

【摘要】：目前学校教学楼内照明等电子设备的开关主要是依靠人工控制,从而造成经常出现“无人亮灯”等情形。根据教室内学生数量及分布,智能控制电力开关,可减少现实中电力资源大量浪费的状况,因而具有重要的意义。论文研究了基于视频的人数统计问题。随着社会经济的发展,越来越多的视频监控系统广泛应用在各类公共场所。如何在这些视频监控数据中提取出有用的信息,是目前该领域的研究热点和难点之一。按照摄像头与目标物体的相对位置人数统计常见的场景有三种：摄像头所处的位置位于目标物体斜上方,能获取目标物体形状轮廓信息、摄像头位于进出的通道口,能够获取目标物体的头部形状轮廓信息以及摄像头位于与目标物体高度大概齐平的位置,能够获取脸部五官特征。这三种场景分别利用三种不同的特征实现统计功能,如利用人体的形状特征、人的头部轮廓特征以及脸部的五官特征。由于教室内的摄像机无法获取完整的人体轮廓信息和清晰的人脸五官信息。本文做了如下工作： 1.本文将基于特征的方法用于教室场景中的人数检测,利用不同的特征提取方法与特征分类方法进行不同的组合实现人数的统计。同时,将稀疏表示的方法用于教室人数统计。并对这些方法进行了实验验证以及比较分析。 2.本文针对教室内学生大多几乎处于静止状态,又偶有身体运动的特点,提出了一种基于矩阵低秩稀疏分解的双背景更新模型。先综合利用帧间差分与低秩稀疏分解预判运动区域,并根据当前视频帧像素与预判的运动区域的位置关系,采用不同的参数更新背景模型；然后利用背景差分法获取运动的目标前景区域；最后对获取的运动目标区域进行二值化、形态学处理、连通区域判断等一系列操作,以消除噪声影响并实现人数统计。实验结果证明,本文提出的改进的方法在教室人数统计中有一定的改善。最后,本文对研究过程中存在的不足进行了分析,并对下一步工作计划做出了阐述。
[Abstract]:At present, the switches of electronic devices such as lighting in school buildings rely on manual control, which often results in "unlit lights" and other situations. According to the number and distribution of students in the classroom, it is of great significance to control the power switch intelligently, which can reduce the waste of power resources in reality. This paper studies the number of people based on video. With the development of social economy, more and more video surveillance systems are widely used in all kinds of public places. How to extract useful information from these video surveillance data is one of the hot and difficult points in this field. According to the relative position of the camera and the target object, there are three common scenes: the camera is located in the position above the target object, it can obtain the contour information of the target object, and the camera is located at the entrance and exit of the target object. It can obtain the contour information of the head of the target object and the position of the camera which is about equal to the height of the target object, and can obtain facial features. The three scenes use three different features to achieve statistical functions, such as the shape of the human body, the contour of the human head and the facial features. Because the camera in the classroom can not obtain complete human contour information and clear facial features information. This paper has done the following work: 1. In this paper, the feature-based method is used to detect the number of students in the classroom scene, and different feature extraction methods and feature classification methods are used to realize the statistics of the number of students. At the same time, the sparse representation method is applied to the classroom population statistics. These methods are verified and compared with each other. 2. In view of the fact that most of the students in the classroom are in a static state and occasionally have the characteristics of physical motion, a dual background updating model based on matrix low rank sparse decomposition is proposed in this paper. First, the motion regions are predetermined by inter-frame difference and low-rank sparse decomposition, and different parameters are used to update the background model according to the position relationship between the current video frame pixels and the pre-determined moving regions. Then the background difference method is used to obtain the foreground region of the moving object. Finally, a series of operations such as binarization, morphological processing, connected region judgment and so on are carried out to eliminate the influence of noise and realize the statistics of the number of people. The experimental results show that the improved method proposed in this paper has a certain improvement in the statistics of classroom population. Finally, this paper analyzes the shortcomings of the research process, and describes the next work plan.
【学位授予单位】：安徽大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN948.6

【参考文献】