无线传感器网络中运动目标协同跟踪技术研究

发布时间：2018-02-24 20:24

本文关键词： 协同跟踪无线传感器网络目标检测行为识别特征提取　出处：《西安电子科技大学》2016年博士论文　论文类型：学位论文

【摘要】：随着嵌入式技术、通信技术和计算机视觉技术的高速发展,无线传感器网络以其先进的理念和广阔的应用前景日益受到学术界的关注,相关技术也成为当前国际上新兴的研究热点之一。运动目标的协同跟踪作为无线传感器网络的一种典型应用一直备受关注,但目前的研究大多针对高空飞行目标,对日常生活中的运动目标例如人体的跟踪则少有涉及。智能视频监控技术是在计算机视觉和图像处理技术上,结合其它相关技术和理论发展起来的一个较新的研究领域,旨在利用计算机或智能处理单元的数据分析能力,自动实现视频场景中静态和动态实物的感知、描述以及分析,满足日常生产、生活中智能安防、智能交通以及城市智慧化建设需要。因此将无线传感器网络应用在智能视频监控系统中,能够实现无线传感器网络对可疑目标的分析和协同跟踪,具有一定的研究及应用价值。基于无线传感器网络的运动目标协同跟踪涉及的知识面较为广泛,按照工作流程主要包含的技术问题和关键步骤有：网络资源的优化部署、运动目标检测和跟踪、目标行为分析、特征提取和匹配、协同跟踪算法等。虽然现有的视频图像分析技术能够解决在某些应用场景下的以上问题,但对于无线传感器网络自身的局限性,例如无线传感器节点对能耗敏感和运算能力有限等问题并不适用,因此本文针对以上提及的关键技术和难点展开研究,具体内容如下：(1)针对无线传感器网络优化部署的效率问题,提出了建立在职能划分基础上的网络优化部署算法。由于智能监控领域对目标的协同跟踪,往往是针对可疑目标来进行的,因此提出了在网络初始化阶段首先区分无线传感器节点的职能,将网络监控点细分为两大类：行为识别监控点和协同跟踪监控点。然后针对系统存在部分可移动协同跟踪监控点的情况,设置行为识别监控点为初始化聚类中心,采用动态模糊聚类算法进行网络优化部署,而对只存在静态监控点的系统采用改进的粒子群优化算法进行网络部署。将监控点的职能划分和优化部署算法相结合的方案,有利于充分发挥无线传感器网络的固有优势,为可疑目标的确定和特征提取打下基础。(2)针对可疑目标的筛选问题以及无线传感器节点的局限性,提出了一种适用于无线传感器网络的运动人体行为识别法。通过行为识别实现可疑目标的定位可分以下几步：运动目标检测、跟踪和行为识别。为了克服运动目标检测中遇到的场景多变的干扰和无线传感器节点运算能力的局限性,采用背景减除法和局部广义霍夫投票相结合的方法进行运动检测,能够较为完整地提取出运动目标区域。而运动目标的跟踪采用基于检测的方法来实现,通过持续的运动目标检测,达到单节点跟踪的目的。最后对于可疑目标的判定问题,提出了建立行为模板库,通过运动目标轮廓小波矩和速度小波矩的提取,结合行为库的模板匹配法来判断目标的行为,若行为异常则确定为待协同跟踪的目标。仅对可疑目标进行协同跟踪,更加符合实际系统的应用需求。(3)针对不同监控点环境差异对运动目标特征提取的影响,而复杂的特征提取算法不适用于无线传感器节点的实际问题,提出了一种多角度数据融合的可疑目标特征提取与匹配算法。首先利用无线传感器网络中监控点存在重复监控区域覆盖的特性,不同角度的监控点将可疑目标轮廓外接矩形内部的像素区域进行超像素分割,对形成的有限个超像素区域进行颜色特征表达,然后将多角度获得的超像素区域颜色特征进行数据融合,得到可疑目标的特征表达。在协同跟踪监控点进行特征匹配时,对当前运动目标进行类似的特征提取,再采用两层匹配法进行特征匹配,由匹配结果判断当前运动目标是否为协同跟踪目标。该方法能够降低不同场景下对同一可疑目标特征提取的误差,提高特征匹配精度。(4)针对无线传感器网络的能耗问题,提出了一种建立在休眠与唤醒机制上的几何监控区域近似和轨迹预测算法。该算法默认网络中的行为识别监控点始终处于工作状态,而协同跟踪监控点处于休眠状态,通过对可疑目标运动轨迹的预测,由行为识别监控点发送命令将涉及协同跟踪的监控点唤醒。此外,针对运算能力问题,尤其是行为识别监控点多目标行为识别和子网管理的运算压力问题,提出了一种基于DOT模型的并行计算思路,最后建立了协同跟踪系统的能耗模型,并通过原型系统的实验和性能仿真实验,结合相似算法的数据对比,说明了本文所研究的协同跟踪算法具有一定的先进性。
[Abstract]:With the rapid development of embedded technology, communication technology and computer vision technology, wireless sensor network with its advanced concept and broad application prospect has attracted the attention of academia, the related technology has also become the new research focus. Moving target collaborative tracking as a typical application of wireless sensor networks has attracted a lot of attention but, most of the current research on high flying target, the moving target in daily life such as the human body tracking are less involved. Intelligent video surveillance technology in computer vision and image processing technology, combined with other related technologies and theories developed in a relatively new field of study, aims to analyze the ability to use a computer or intelligent data processing unit, automatic realization of static and dynamic physical perception of the video scene, description and analysis, to meet the daily Production, intelligent life, intelligent transportation and intelligent city construction. So the application of the wireless sensor network in intelligent video surveillance system, can realize the wireless sensor network for suspicious target analysis and collaborative tracking, has a certain value of research and application of wireless sensor network. The moving target tracking involves more collaborative knowledge based on extensive, according to the technical problems and key steps of work process mainly include: optimizing the deployment of cyber source, moving target detection and tracking, target behavior analysis, feature extraction and matching, collaborative tracking algorithm. Although the existing video image analysis technology can solve the above problems in some application scenarios, but for limitations the wireless sensor network, such as wireless sensor nodes for sensitive and operational problems such as limited energy consumption and not applicable, Therefore, aiming at the key technology and difficulty of the above mentioned research, the specific contents are as follows: (1) aiming at the efficiency problem of optimal deployment of wireless sensor network, proposed the establishment of functional network deployment optimization algorithm based on the division of the field of intelligent monitoring. Because of synergistic tracking for the target is often carried out according to the suspicious target, so put forward in the network initialization phase we distinguish wireless sensor node functions, the network monitoring points is subdivided into two categories: behavior identification monitoring point and monitoring points. Then according to the collaborative tracking system is part of mobile collaborative tracking and monitoring points, set up monitoring points for behavior recognition to initialize cluster centers by dynamic network optimization deployment fuzzy clustering algorithm, and the system exists only static monitoring points based on Improved Particle Swarm Optimization Algorithm for network deployment monitoring. The combination of function and optimization deployment algorithm point scheme, is conducive to give full play to the inherent advantages of wireless sensor network, and determine the characteristics of suspicious target extraction basis. (2) screening of suspicious targets and to solve the problem of wireless sensor node the limitations of human motion recognition method is proposed for the wireless sensor network. Locate the suspicious target through behavior recognition can be divided into the following steps: moving target detection, tracking and behavior recognition. In order to overcome the interference encountered in the moving target detection and scene changing wireless sensor nodes the limitations of the method of using the method of background subtraction and local generalized Hof voting combination motion detection, can accurately extract the moving target area. While tracking the moving target detection method based on the achieved through continued The moving target detection, to achieve the purpose of tracking the single node. Finally the suspicious target decision problem, proposed the establishment of a behavior template library, by extracting the contour of the moving target speed of wavelet moment and wavelet moment matching method, to determine the target binding behavior library template, if the abnormal behavior is determined for collaborative target tracking. Cooperative tracking of suspicious targets, more in line with the application of the actual needs of the system. (3) the effects of different environmental monitoring points difference of moving target feature extraction, feature extraction algorithm for complex practical problems are not suitable for wireless sensor nodes, the proposed algorithm extracting and matching the suspicious target feature a multi angle data fusion the first use of monitoring points. The wireless sensor network has characteristics of repetitive coverage of monitoring area, monitoring will be suspicious object contour from different angles of internal external rectangle The pixel area of the pixel segmentation, the formation of a finite super pixel region color feature representation, and then the super pixel region color multi angle characteristics obtained by data fusion, feature expression of suspicious targets. In the collaborative tracking and monitoring point feature matching, the moving target is similar to feature extraction. The two layer matching method for feature matching, the matching results determine whether the current target for collaborative target tracking. This method can reduce the error of the same suspicious target feature extraction in different scenarios, improve the feature matching accuracy. (4) to solve the problem of power consumption in wireless sensor networks, a set up in dormancy and wake up regional monitoring mechanism on the geometric approximation and prediction algorithms. The algorithm is the default behavior identification monitoring point in the network is always in a working state and cooperative tracking supervision The control points in a dormant state, the prediction of the suspicious target track, by sending behavior recognition monitoring point command will involve monitoring point tracking cooperative wake up. In addition, according to the operation ability, especially the operation pressure monitoring point multi object behavior recognition behavior recognition and sub network management, this paper presents a calculation method of parallel based on the DOT model, the energy consumption model of cooperative tracking system is established, and through the simulation experiment and the performance of the prototype system, compared with the similarity algorithm, the cooperative tracking algorithm in this paper is advanced.

【学位授予单位】：西安电子科技大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：TP212.9;TN929.5

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