导　　师： 李学龙

学科专业： 081002

授予学位： 硕士

作　　者： ;

机构地区： 中国科学院研究生院

摘　　要： 随着社会的进步，人们对于公共安全区域监控系统的需求日益强烈。而计算机技术的迅猛发展，则有力地推进了智能监控系统的基础研究与应用推广工作。作为智能监控系统中两个关键性问题，活动相关性分析与行人再识别得到了广大科研工作者的深入研究。 活动相关性分析是指分析各个摄像机覆盖区域中子区域的相关性和时间延迟。实际应用中，我们可以由此得到摄像机之间的拓扑结构，为后续行人再识别建立稳定的基础。行人再识别是指将视频监控中单个摄像机下的行人检测结果作为输入，在其他摄像机下的行人检测结果中进行同一行人的匹配过程。这在实际的安防监控中有着极大的用处。 在实际公共安全区域的应用中，活动相关性分析的难点在于很难从拥挤的人群和车辆中提取有效且稳定的活动特征，这是相关性分析的基础，只有稳定的特征才能够学习出有实际意义的相关性结果。针对这一问题，本文提出了一种基于慢特征分析的方法，将其应用到底层活动特征以获得更有意义的高层稳定特征。 行人再识别的难点在于如何选择一个最优的度量方式对不同摄像机下的行人进行匹配。因为不同摄像机角度光照等差异，以及行人姿态的变化，同一行人在不同摄像机下的视觉特征相差会非常大。为了解决这一问题，本文提出了一种基于稀疏编码的度量方式，通过训练集中同一行人的样本对来构建字典，以此消除不同摄像机角度等变化带来的系统性差异，再利用学习到的字典对待检测样本和备选集样本对进行重构，判定重构误差最小的一对样本即是同一个行人。 为了验证本文提出的两个方法的有效性，本文对两个方法分别在一个三岔路口的视频数据以及三个公开的行人再识别数据库/(i-LIDS，VIPeR以及GRID/)上进行了实验验证，并和各自领域内的最优算法进行对比。实验结果表明本文提出的两个算法相比于现有算法更具备有效性和优越性。 With the development of the society, people demand increasingly strong regionalmonitoring system for public safety. In recent years, the rapid developments ofcomputer technology effectively promote the basic research and applicationpromotion of intelligent multi-camera video surveillance system. As two keyproblems of the intelligent video surveillance system, activity correlation analysis andperson re-identification attract vast number of scientific research workers. Activity correlation analysis refers to analysis of the correlation of each cameraneutron area coverage and time delay. In practical application, we can get the cameratopology, for subsequent person re-identification problem again. While personre-identification refers to regarding the video monitoring in pedestrian detectionresults under a single camera as an input, then match it with the pedestrian detectionresults under the other cameras. This has greatly apply in the security monitoring. In practical application of public security area, the key point of activitycorrelation analysis is that it's difficult to extract both effective and stable features ofactivity generated from the crowds and vehicles, which is the basis of correlationanalysis; only the stable feature has actual significance to study the useful correlation.In order to solve this problem, this paper proposes a method based on slow featureanalysis /(SFA/) and applies it to the low-level feature of activity in order to obtainmore meaningful and stable high-lever feature representation. While the difficulty of person re-identification lies in how to choose an optimalmeasure to match pedestrians of different cameras. Because of changes from theillumination, angle differences under different cameras, and person’s posture, thesame persons from different cameras will have very big difference in visualcharacteristics. In order to solve this problem, this paper proposes a sparse codingbased measure. Through the training the relative distances of pairwise samples from same pedestrian captured in different cameras to obtain a dictionary, systemicdifferences of different cameras can be eliminated at the same time. Then relativedistances pairs of probe samples and gallery samples will be reconstructed with thelearned dictionary, and the reconstruction error is a criterion to measure the result ofmatching. The smallest error of pairs will be regard as matched pairs. In order to verify the effectiveness of the two methods proposed in this paper,they are investigated on the video data in a fork road and three public personre-identification databases /(i-LIDS, VIPeR, and GRID/) for experimental verification,and compared with the state-of-the-art methods in individual field respectively. Theexperimental results show the effectiveness and superiority of the two proposedalgorithms.

关 键 词： 智能监控 活动相关性分析 行人再识别 慢特征 稀疏编码

分 类 号： [TP277]

领　　域： [自动化与计算机技术] [自动化与计算机技术]