本文選題：GIS　切入點(diǎn)：視頻　出處：《南京師范大學(xué)》2014年博士論文

【摘要】：地理場景具有高動(dòng)態(tài)性、多尺度性和不確定性等特點(diǎn),研究動(dòng)態(tài)目標(biāo)的實(shí)時(shí)感知方法,快速智能感知地理場景中動(dòng)態(tài)目標(biāo)時(shí)空特征,基于此探索動(dòng)態(tài)目標(biāo)的行為規(guī)律,已成為當(dāng)前學(xué)術(shù)界和政府管理部門亟待解決的問題。 目前,視頻監(jiān)控系統(tǒng)以其高清實(shí)時(shí)、功能智能、價(jià)格低廉等優(yōu)勢在安全領(lǐng)域發(fā)揮著越來越重要的作用。然而,現(xiàn)有基于監(jiān)控視頻的動(dòng)態(tài)目標(biāo)跟蹤局限于二維圖像空間,無法感知其在真實(shí)地理場景中的時(shí)空特征,對(duì)于具有大量視頻監(jiān)控設(shè)備的區(qū)域,不能實(shí)現(xiàn)多攝像機(jī)對(duì)動(dòng)態(tài)目標(biāo)的協(xié)同跟蹤。鑒于此,本文以監(jiān)控視頻與地理場景的協(xié)同分析為手段,針對(duì)地理場景下動(dòng)態(tài)目標(biāo)狀態(tài)的感知這一科學(xué)問題,發(fā)展地理場景約束下的動(dòng)態(tài)目標(biāo)時(shí)空信息提取與分析方法,取得了以下研究成果： (1)提出了多平面約束下的監(jiān)控視頻與2D地理空間數(shù)據(jù)幾何互映射模型�，F(xiàn)有監(jiān)控視頻至地理空間的映射模型假定地面為單一高程,當(dāng)監(jiān)控區(qū)域存在多個(gè)不同的高程平面時(shí),現(xiàn)有方法需重新求解互映射矩陣,過程較繁瑣。本文通過對(duì)比分析攝影測量學(xué)與計(jì)算機(jī)視覺的相機(jī)模型,構(gòu)建了適用于固定攝像機(jī)與PTZ攝像機(jī)的監(jiān)控視頻與2D地理空間數(shù)據(jù)幾何互映射模型,該模型具有各參數(shù)物理意義明確、理論嚴(yán)密、靈活性強(qiáng)等特點(diǎn)。 (2)提出了基于深度的監(jiān)控視頻與3D地理空間數(shù)據(jù)幾何互映射模型。從三維空間坐標(biāo)到圖像坐標(biāo)的轉(zhuǎn)換可基于針孔成像模型來實(shí)現(xiàn),但從圖像坐標(biāo)到三維空間坐標(biāo)的轉(zhuǎn)換,現(xiàn)有方法主要通過視線與3D模型相交的方式來實(shí)現(xiàn),其計(jì)算量大且過程繁瑣。本文基于3D地理空間數(shù)據(jù)可視化過程中的緩存深度值構(gòu)建了監(jiān)控視頻與3D地理空間數(shù)據(jù)的互映射模型。與傳統(tǒng)方法相比,本模型具有過程簡潔,效率更高,可實(shí)現(xiàn)監(jiān)控視頻與3D地理空間數(shù)據(jù)間實(shí)時(shí)同步的動(dòng)態(tài)互映射。 (3)提出了一種監(jiān)控視頻與地理空間數(shù)據(jù)的半自動(dòng)互映射方法。本文系統(tǒng)分析了傳統(tǒng)的基于單應(yīng)的幾何互映射方法,并進(jìn)行了不確定性分析。鑒于地理空間數(shù)據(jù)精度日益提高,本文基于結(jié)構(gòu)化地理場景的約束,設(shè)定滅點(diǎn)相似性、特征線相似性兩個(gè)指標(biāo)作為匹配的依據(jù),探討了監(jiān)控視頻與2D/3D地理空間數(shù)據(jù)視圖的半自動(dòng)匹配方法。 (4)設(shè)計(jì)了一種面向監(jiān)控視頻的前景目標(biāo)時(shí)空信息提取方法�；诒O(jiān)控視頻與地理空間數(shù)據(jù)互映射模型,提出了地理場景中監(jiān)控視頻前景目標(biāo)時(shí)空信息的提取方法,包括目標(biāo)方位信息、幾何信息、目標(biāo)軌跡、前景圖像等,基于面向?qū)ο笏枷虢⒘似鋵?duì)應(yīng)的時(shí)空數(shù)據(jù)模型,實(shí)現(xiàn)了對(duì)前景目標(biāo)數(shù)據(jù)的管理與GIS集成。 (5)構(gòu)建了一種基于路網(wǎng)約束的盲區(qū)目標(biāo)軌跡估計(jì)模型。監(jiān)控?cái)z像機(jī)通常布設(shè)在相對(duì)重要的位置,具有獨(dú)立性、分散性等特點(diǎn),完全基于監(jiān)控視頻無法感知監(jiān)控目標(biāo)的連續(xù)運(yùn)動(dòng)軌跡。現(xiàn)有多攝像機(jī)協(xié)同下的目標(biāo)連續(xù)跟蹤,多利用監(jiān)控視頻場景間具有重疊條件下開展研究,不適合大場景中目標(biāo)的連續(xù)跟蹤。本文以場景中動(dòng)態(tài)目標(biāo)的時(shí)空信息及基礎(chǔ)地理信息為數(shù)據(jù)基礎(chǔ),提出了路網(wǎng)(及目標(biāo)行為規(guī)則)約束下的動(dòng)態(tài)目標(biāo)連續(xù)跟蹤方法。 (6)研發(fā)了地理場景中多攝像機(jī)協(xié)同的動(dòng)態(tài)目標(biāo)連續(xù)跟蹤原型系統(tǒng)�；谝陨涎芯砍晒�,設(shè)計(jì)并開發(fā)了動(dòng)態(tài)目標(biāo)連續(xù)跟蹤原型系統(tǒng),該系統(tǒng)具有監(jiān)控視頻與地理空間數(shù)據(jù)的互映射、動(dòng)態(tài)目標(biāo)時(shí)空信息提取、動(dòng)態(tài)目標(biāo)跟蹤及可視化、大場景下監(jiān)控目標(biāo)軌跡估算等功能。
[Abstract]:Geographic scenes with high dynamic characteristics, multi scale and uncertainty, real-time perception of dynamic target, temporal and spatial characteristics of fast dynamic target intelligence geographic scene, so as to explore the dynamic behavior based on the target, has become the current academic circles and the government management problems to be solved.
At present, the HD video monitoring system with real-time, intelligent, low price advantage plays an increasingly important role in the field of security. However, the existing dynamic target tracking based on video is limited to 2D image space, not in the real sense of its geographical scene in the temporal and spatial features, for having a large number of video surveillance equipment area and can not realize the multi camera collaborative tracking of dynamic objects. In view of this, based on the analysis of cooperative surveillance video and geographical scene as a means of this science to solve the problem of dynamic target state under geographical scene perception, dynamic spatio-temporal information extraction and analysis method for the development of geographic scene constraints, obtained the following research results:
(1) put forward the monitoring video and 2D spatial data geometric Multi Planar Constraints mutual mapping model. The mapping model of existing video surveillance to the geographic space for a single assumed ground elevation, while there are many different elevation of the monitoring area, the existing methods need to solve mutual mapping matrix, the process is trivial. Comparative analysis of photogrammetry and computer vision camera model, constructed the monitoring video and 2D spatial data geometry for fixed camera and PTZ camera mutual mapping model, the model has clear physical meaning of each parameter, strict theory, flexibility and so on.
(2) put forward the monitoring video and 3D spatial data model based on the geometric depth of mutual mapping. Conversion from 3D coordinates to image coordinates can be realized based on pinhole imaging model, but from the image coordinate to three-dimensional space coordinate transformation, the existing methods mainly through the intersection line of sight and the model of 3D to realize the calculation. The amount is large and complicated process. The buffer depth of 3D spatial data visualization process based on the value set up a mapping model of surveillance video and 3D spatial data. Compared with the traditional method, this model has simple process, high efficiency, can realize video monitoring and 3D spatial data dynamic real time synchronization mutual mapping.
(3) proposed a surveillance video and geospatial data interoperability. Semi automatic mapping method based on the analysis of the geometry of single should be mutual mapping method based on the traditional, and the uncertainty analysis. In view of the accuracy of spatial data is increasing, the structured geographic scene based on the constraint of vanishing point, set similarity, feature line two similarity index as the matching basis, discusses the monitoring video and 2D/3D geospatial data view semi automatic matching method.
(4) design a method to extract foreground objects spatial information oriented video surveillance video and geospatial data interoperability mapping model based on the proposed extraction method of monitoring video object spatial information in geographical scene, including the target azimuth information, geometric information, target trajectory, foreground image, object oriented spatio temporal data the corresponding model is established based on the realization of the management and the GIS data of foreground object integration.
(5) constructs a blind estimation model based on target trajectory constraints. Road surveillance cameras are usually deployed in the relatively important position is independent, dispersion characteristics, based on continuous motion video monitoring target. Unable to perceive the continuous tracking of the existing multi camera collaborative target under multi use surveillance video scene with overlapping conditions to carry out research, continuous tracking is not suitable for large scene. In this paper the dynamic target in the scene and temporal information of basic geographic information data base, put forward the network (and rules) for dynamic target tracking under the constraint method.
(6) developed a prototype system of continuous tracking dynamic target geographic scene in multi camera collaboration. Based on the above research, design and development of dynamic object continuous tracking prototype system, the system of mutual mapping with monitoring video and geospatial data extraction, dynamic target spatio-temporal information, dynamic target tracking and visualization, target trajectory estimation the function of monitoring the large scene.

【學(xué)位授予單位】：南京師范大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：P208

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本文編號(hào)：1669506