發(fā)布時(shí)間：2018-02-24 20:24

  本文關(guān)鍵詞： 協(xié)同跟蹤 無(wú)線傳感器網(wǎng)絡(luò) 目標(biāo)檢測(cè) 行為識(shí)別 特征提取　出處：《西安電子科技大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：隨著嵌入式技術(shù)、通信技術(shù)和計(jì)算機(jī)視覺(jué)技術(shù)的高速發(fā)展,無(wú)線傳感器網(wǎng)絡(luò)以其先進(jìn)的理念和廣闊的應(yīng)用前景日益受到學(xué)術(shù)界的關(guān)注,相關(guān)技術(shù)也成為當(dāng)前國(guó)際上新興的研究熱點(diǎn)之一。運(yùn)動(dòng)目標(biāo)的協(xié)同跟蹤作為無(wú)線傳感器網(wǎng)絡(luò)的一種典型應(yīng)用一直備受關(guān)注,但目前的研究大多針對(duì)高空飛行目標(biāo),對(duì)日常生活中的運(yùn)動(dòng)目標(biāo)例如人體的跟蹤則少有涉及。智能視頻監(jiān)控技術(shù)是在計(jì)算機(jī)視覺(jué)和圖像處理技術(shù)上,結(jié)合其它相關(guān)技術(shù)和理論發(fā)展起來(lái)的一個(gè)較新的研究領(lǐng)域,旨在利用計(jì)算機(jī)或智能處理單元的數(shù)據(jù)分析能力,自動(dòng)實(shí)現(xiàn)視頻場(chǎng)景中靜態(tài)和動(dòng)態(tài)實(shí)物的感知、描述以及分析,滿足日常生產(chǎn)、生活中智能安防、智能交通以及城市智慧化建設(shè)需要。因此將無(wú)線傳感器網(wǎng)絡(luò)應(yīng)用在智能視頻監(jiān)控系統(tǒng)中,能夠?qū)崿F(xiàn)無(wú)線傳感器網(wǎng)絡(luò)對(duì)可疑目標(biāo)的分析和協(xié)同跟蹤,具有一定的研究及應(yīng)用價(jià)值�；跓o(wú)線傳感器網(wǎng)絡(luò)的運(yùn)動(dòng)目標(biāo)協(xié)同跟蹤涉及的知識(shí)面較為廣泛,按照工作流程主要包含的技術(shù)問(wèn)題和關(guān)鍵步驟有：網(wǎng)絡(luò)資源的優(yōu)化部署、運(yùn)動(dòng)目標(biāo)檢測(cè)和跟蹤、目標(biāo)行為分析、特征提取和匹配、協(xié)同跟蹤算法等。雖然現(xiàn)有的視頻圖像分析技術(shù)能夠解決在某些應(yīng)用場(chǎng)景下的以上問(wèn)題,但對(duì)于無(wú)線傳感器網(wǎng)絡(luò)自身的局限性,例如無(wú)線傳感器節(jié)點(diǎn)對(duì)能耗敏感和運(yùn)算能力有限等問(wèn)題并不適用,因此本文針對(duì)以上提及的關(guān)鍵技術(shù)和難點(diǎn)展開研究,具體內(nèi)容如下：(1)針對(duì)無(wú)線傳感器網(wǎng)絡(luò)優(yōu)化部署的效率問(wèn)題,提出了建立在職能劃分基礎(chǔ)上的網(wǎng)絡(luò)優(yōu)化部署算法。由于智能監(jiān)控領(lǐng)域?qū)δ繕?biāo)的協(xié)同跟蹤,往往是針對(duì)可疑目標(biāo)來(lái)進(jìn)行的,因此提出了在網(wǎng)絡(luò)初始化階段首先區(qū)分無(wú)線傳感器節(jié)點(diǎn)的職能,將網(wǎng)絡(luò)監(jiān)控點(diǎn)細(xì)分為兩大類：行為識(shí)別監(jiān)控點(diǎn)和協(xié)同跟蹤監(jiān)控點(diǎn)。然后針對(duì)系統(tǒng)存在部分可移動(dòng)協(xié)同跟蹤監(jiān)控點(diǎn)的情況,設(shè)置行為識(shí)別監(jiān)控點(diǎn)為初始化聚類中心,采用動(dòng)態(tài)模糊聚類算法進(jìn)行網(wǎng)絡(luò)優(yōu)化部署,而對(duì)只存在靜態(tài)監(jiān)控點(diǎn)的系統(tǒng)采用改進(jìn)的粒子群優(yōu)化算法進(jìn)行網(wǎng)絡(luò)部署。將監(jiān)控點(diǎn)的職能劃分和優(yōu)化部署算法相結(jié)合的方案,有利于充分發(fā)揮無(wú)線傳感器網(wǎng)絡(luò)的固有優(yōu)勢(shì),為可疑目標(biāo)的確定和特征提取打下基礎(chǔ)。(2)針對(duì)可疑目標(biāo)的篩選問(wèn)題以及無(wú)線傳感器節(jié)點(diǎn)的局限性,提出了一種適用于無(wú)線傳感器網(wǎng)絡(luò)的運(yùn)動(dòng)人體行為識(shí)別法。通過(guò)行為識(shí)別實(shí)現(xiàn)可疑目標(biāo)的定位可分以下幾步：運(yùn)動(dòng)目標(biāo)檢測(cè)、跟蹤和行為識(shí)別。為了克服運(yùn)動(dòng)目標(biāo)檢測(cè)中遇到的場(chǎng)景多變的干擾和無(wú)線傳感器節(jié)點(diǎn)運(yùn)算能力的局限性,采用背景減除法和局部廣義霍夫投票相結(jié)合的方法進(jìn)行運(yùn)動(dòng)檢測(cè),能夠較為完整地提取出運(yùn)動(dòng)目標(biāo)區(qū)域。而運(yùn)動(dòng)目標(biāo)的跟蹤采用基于檢測(cè)的方法來(lái)實(shí)現(xiàn),通過(guò)持續(xù)的運(yùn)動(dòng)目標(biāo)檢測(cè),達(dá)到單節(jié)點(diǎn)跟蹤的目的。最后對(duì)于可疑目標(biāo)的判定問(wèn)題,提出了建立行為模板庫(kù),通過(guò)運(yùn)動(dòng)目標(biāo)輪廓小波矩和速度小波矩的提取,結(jié)合行為庫(kù)的模板匹配法來(lái)判斷目標(biāo)的行為,若行為異常則確定為待協(xié)同跟蹤的目標(biāo)。僅對(duì)可疑目標(biāo)進(jìn)行協(xié)同跟蹤,更加符合實(shí)際系統(tǒng)的應(yīng)用需求。(3)針對(duì)不同監(jiān)控點(diǎn)環(huán)境差異對(duì)運(yùn)動(dòng)目標(biāo)特征提取的影響,而復(fù)雜的特征提取算法不適用于無(wú)線傳感器節(jié)點(diǎn)的實(shí)際問(wèn)題,提出了一種多角度數(shù)據(jù)融合的可疑目標(biāo)特征提取與匹配算法。首先利用無(wú)線傳感器網(wǎng)絡(luò)中監(jiān)控點(diǎn)存在重復(fù)監(jiān)控區(qū)域覆蓋的特性,不同角度的監(jiān)控點(diǎn)將可疑目標(biāo)輪廓外接矩形內(nèi)部的像素區(qū)域進(jìn)行超像素分割,對(duì)形成的有限個(gè)超像素區(qū)域進(jìn)行顏色特征表達(dá),然后將多角度獲得的超像素區(qū)域顏色特征進(jìn)行數(shù)據(jù)融合,得到可疑目標(biāo)的特征表達(dá)。在協(xié)同跟蹤監(jiān)控點(diǎn)進(jìn)行特征匹配時(shí),對(duì)當(dāng)前運(yùn)動(dòng)目標(biāo)進(jìn)行類似的特征提取,再采用兩層匹配法進(jìn)行特征匹配,由匹配結(jié)果判斷當(dāng)前運(yùn)動(dòng)目標(biāo)是否為協(xié)同跟蹤目標(biāo)。該方法能夠降低不同場(chǎng)景下對(duì)同一可疑目標(biāo)特征提取的誤差,提高特征匹配精度。(4)針對(duì)無(wú)線傳感器網(wǎng)絡(luò)的能耗問(wèn)題,提出了一種建立在休眠與喚醒機(jī)制上的幾何監(jiān)控區(qū)域近似和軌跡預(yù)測(cè)算法。該算法默認(rèn)網(wǎng)絡(luò)中的行為識(shí)別監(jiān)控點(diǎn)始終處于工作狀態(tài),而協(xié)同跟蹤監(jiān)控點(diǎn)處于休眠狀態(tài),通過(guò)對(duì)可疑目標(biāo)運(yùn)動(dòng)軌跡的預(yù)測(cè),由行為識(shí)別監(jiān)控點(diǎn)發(fā)送命令將涉及協(xié)同跟蹤的監(jiān)控點(diǎn)喚醒。此外,針對(duì)運(yùn)算能力問(wèn)題,尤其是行為識(shí)別監(jiān)控點(diǎn)多目標(biāo)行為識(shí)別和子網(wǎng)管理的運(yùn)算壓力問(wèn)題,提出了一種基于DOT模型的并行計(jì)算思路,最后建立了協(xié)同跟蹤系統(tǒng)的能耗模型,并通過(guò)原型系統(tǒng)的實(shí)驗(yàn)和性能仿真實(shí)驗(yàn),結(jié)合相似算法的數(shù)據(jù)對(duì)比,說(shuō)明了本文所研究的協(xié)同跟蹤算法具有一定的先進(jìn)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.

【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP212.9;TN929.5

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本文編號(hào)：1531591