發(fā)布時間：2018-10-23 15:13

【摘要】：視頻GIS是當前地理信息科學的研究熱點之一。傳感器和計算機視覺技術(shù)的快速發(fā)展，以及多樣終端、異構(gòu)網(wǎng)絡(luò)和海量數(shù)據(jù)的涌現(xiàn)，為視頻GIS帶來了新的機遇和挑戰(zhàn)。如何實現(xiàn)地理視頻解析過程的自動化和智能化，以適配復雜的應(yīng)用環(huán)境，是視頻GIS亟待解決的問題。目前，視頻解析常用的數(shù)據(jù)驅(qū)動方法能夠改善基于模型的方法難以解決的多事例、多樣性和多模態(tài)等問題，并能有效挖掘信息、學習知識。但是，其提取的特征僅局限于底層特征，難以反映高層語義，地理視頻解析中存在的“語義鴻溝”仍待解決。同時，視頻運動要素的行為通常與地理環(huán)境密切相關(guān)，考慮空間約束可增強其行為理解的準確性。 為此，本文基于視頻運動要素完備性定義，采用數(shù)值化方法描述地理空間中視頻運動要素的相互作用關(guān)系。構(gòu)建以隨機圖動態(tài)分析模型和演化規(guī)則為基礎(chǔ)，以地理視頻內(nèi)容的結(jié)構(gòu)化描述為核心，以實現(xiàn)地理視頻流解析的自動化、智能化為目標的方法體系。具體工作主要包括： (1)綜合分析地理視頻解析所涉及的地理視頻編碼、視頻智能解析和基于邊的隨機圖三個關(guān)鍵技術(shù)，討論地理空間認知并給出地理視頻空間認知地圖。 (2)準確定義視頻運動要素相關(guān)概念，引入單向時間維劃分廣義、狹義地理空間距離。在此基礎(chǔ)上，分析視頻運動要素相互作用關(guān)系的動態(tài)特性及其數(shù)值計算方法�；�于上下文相關(guān)隨機圖語法，建立稀疏隨機圖動態(tài)分析模型，詳細描述融合時空語義信息的隨機圖動態(tài)演化過程。 (3)引入地理空間約束進行視頻場景區(qū)域分割，給出基于單幀的視頻運動要素空間關(guān)系定性表示方法。描述視頻運動要素空間關(guān)系的連續(xù)變化過程，利用隨機圖演化規(guī)則，，建立可全局觀察和分析的隨機圖動態(tài)演化模型。詳細分析可結(jié)構(gòu)化表達地理視頻內(nèi)容變化過程的SRG地理視頻特征文件，并給出地理視頻內(nèi)容可視化解析模型。 (4)以視頻監(jiān)控數(shù)據(jù)集為例，對本文提出的思路進行實例驗證，初步實現(xiàn)地理視頻智能解析。 實踐結(jié)果表明，本文提出的方法能動態(tài)、直觀地描述地理視頻流中運動要素的空間關(guān)系，為地理視頻場景的語義描述與智能解析提供一種新的思路。
[Abstract]:Video GIS is one of the research hotspots in geographic information science. The rapid development of sensor and computer vision technology, as well as the emergence of multiple terminals, heterogeneous networks and massive data, bring new opportunities and challenges to video GIS. How to realize the automation and intelligence of geographic video parsing process to adapt to the complex application environment is the urgent problem of video GIS. At present, the commonly used data-driven methods for video parsing can improve the multi-case, diversity and multi-modal problems that are difficult to solve by model-based methods, and can effectively mine information and learn knowledge. However, the extracted features are limited to the underlying features, which are difficult to reflect the high-level semantics. The "semantic gap" in geographic video parsing still needs to be solved. At the same time, the behavior of video motion elements is usually closely related to the geographical environment, and spatial constraints can enhance the accuracy of their behavior understanding. Therefore, based on the definition of the completeness of video motion elements, a numerical method is used to describe the interaction of video motion elements in geographic space. Based on the stochastic graph dynamic analysis model and evolution rules, this paper constructs a method system based on structured description of geographic video content, which aims at automating and intelligently analyzing geographic video stream. The main work includes: (1) synthetically analyzing the three key technologies of geographic video coding, intelligent video parsing and edge-based random graph. This paper discusses geospatial cognition and gives geographic video spatial cognitive map. (2) the concept of video motion elements is defined accurately, and the generalized division of one-way temporal dimension and narrow geographical space distance are introduced. On this basis, the dynamic characteristics of the interaction of video motion elements and its numerical calculation method are analyzed. Based on context-dependent random graph syntax, a sparse random graph dynamic analysis model is established to describe the dynamic evolution process of random graph with temporal and spatial semantic information in detail. (3) Geo-spatial constraints are introduced to segment video scene region. A qualitative representation method of spatial relationship of video motion elements based on single frame is presented. This paper describes the continuous changing process of spatial relation of video motion elements, and establishes a dynamic evolution model of random graph which can be observed and analyzed globally by using the evolution rule of random graph. The SRG geographic video feature file which can structurally express the changing process of geographic video content is analyzed in detail, and a visual analysis model of geographic video content is given. (4) taking video surveillance data set as an example, The method proposed in this paper is verified by an example, and the intelligent analysis of geographic video is preliminarily realized. The practical results show that the proposed method can dynamically and intuitively describe the spatial relationship of motion elements in geographic video streams and provide a new way of thinking for semantic description and intelligent analysis of geographic video scenes.
【學位授予單位】：重慶郵電大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：P208

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