基于简单固定背景图像特征的人数统计

重庆大学硕士学位论文 基于简单固定背景图像特征的人数统计

硕士研究生： 指 导 教 师： 教 授 学科、专业： 电路与系统

重庆大学通信工程学院 二OO七年四月 Master Degree Dissertation of Chongqing University Total Coloring and Adjacent Vertex-distinguish Total Coloring of Graph

M.D. Candidate: Supervisor: Major: Electrical Circuits and System

College of Conmunication Engineering Chongqing University April 2007重庆大学硕士学位论文 中文摘要 I 摘 要 基于图象处理的机器视觉与智能识别技术是图象处理的前沿和人工智能研究领域的重要分支，机器识别的方法在于研究合适的计算模型，然后利用机器来实现简单的智能判决。本文以“人”目标的检测为研究对象，采用归一化方案和主动轮廓算法提取采样图象中的目标信息，即图象特征，并利用一定的先验知识对该信息进行判断，达到检测和识别“人”目标的目的。 目标检测是识别的前提工作，本文通过加权灰度分析寻找一个可以逼近我们感兴趣目标的图象点（即目标点），并在此基础上建立直角坐标系，分割采样图，解决多个目标检测的问题。 目标匹配是目标跟踪，识别领域的基本思想，在本课题中鉴于采样图获取目标的位置，角度和形态差异，直接进行目标匹配显然是不可行的。本文采用归一化的思想，通过角度修正，坐标平移和坐标伸缩，得到目标的标准尺寸和形态。与传统归一化算法的区别在于，本文并不采用某个标准模板作为参考，因而可以防止非“人”目标在归一化之后具有和“人”目标相似的尺寸和形态。 轮廓检测一直是目标的识别算法中最基本也最困难的问题，传统的轮廓检测方案往往受采样图的实际效果影响较大，造成提取轮廓模糊或存在断点乃至严重的轮廓失真。通常的解决方案是采用相应的后续修正算法，让目标轮廓尽量保持连续。本文在目标归一化的基础上，采用主动轮廓算法，较好的解决了轮廓对目标的贴近及其本身连续性问题，避免了复杂的后续修正工作。 通常情况下的目标识别往往可以对识别过的目标进行标记或能很快提取其不变特征，不至于影响其他目标判断。然而“人”目标的形态会随时间发生随机的改变，不容易提取其不变性特征，鉴于人数统计的客观要求，不能容许对同一个人重复计数。本文通过对上一次目标检测和目标识别的结果以及当前目标检测结果进行综合验算的方法，来判断是否对当前采样图进行识别判断，在解决了重复计数问题的同时大大降低了系统的实时处理数据量。

关键词： 目标检测 归一化 主动轮廓 计数 重庆大学硕士学位论文 英文摘要

II ABSTRACT The technology based on image processing and machine vision is in the forefront of the Intelligent Recognition of image processing and it is the important research branch of artificial intelligence plant. The identificational method is to find a suitable model, and then using machines to achieve simple intelligent judgment. In this paper, we study the recognition and judgement of the target of human beings. We use the normalization program and an active contour to extract algorithm information of the object and image characteristics from sampling image .Then, we make use of some prior knowledge of this information to reach the judgement which cause to the identification of "people" Goals. Target detection is the premise of recognition. According to Gray weighted analysis we can find an interesting target which is close to us the image point (that may be the goal points). On basis of this, we can establishmen a Cartesian coordinates and segment from the sampling image to solve the numbers of targets detection problem. Target matching and target tracking is the basic ideology of identification area, in the light of the issue of uncertained sampling image.It is hard to gain the position, Morphological differences in perspective .So matching the target directly is obviously not feasible. In this paper, incording to the idea of normalization program, such as angle correction, coordinate translation and telescopic coordinates, we can obtain standard sizes and shapes of objects. In a different with traditional algorithm ,it doesn’t use standard templates as a reference. thus we can prevent the other targets from getting similar size and morphology of “people” targets. Contour detection is always be the is the most fundamental and most difficult problem of objective recognition algorithm, traditional contour detection program often suffers from the inactual results of sampling map greatly, which causes Fuzzy result of contour extraction and breakpoints in contour or even serious distortion of the contours. The usual solution is to adopt corresponding amendments to the follow-up algorithms for object contours to maintain continuous. Based on the goal of normalization and active contour algorithm issue in this paper, wo can solve the problem to get profile of the target close to his own continuity and void the complex follow-up rectification works. Under normal circumstances , the target recognition can often be solved by 重庆大学硕士学位论文 英文摘要 III identifing the target or extracting its invariant feature as soon as possible. Thus, it will not affect judgment of other goals. However, "people" target patterns will have random changes over time. So it is not easy to extract its invariant features. Giving the statistical number of objectives is not allowed to repeat the same individual counts. Based on result of previous target detection and the identification of goals in current , we can use the integrated calculation method to determine the necessity of identifing and judging of the current sampling plan . In a result , we solve the problem of counting repeatly and reduce the amount of date in a real-time processing.

Keywords: target detecting , normalization, active contour, count.