本文選題：場景流 + RGBD圖像序列��； 參考：《南昌航空大學》2017年碩士論文

【摘要】：3D場景流是空間場景或物體運動的三維運動矢量場,其包含了場景或物體的三維運動與結構信息,在目標運動估計與跟蹤、姿態(tài)識別、自主避障、路徑規(guī)劃等研究方向具有重要的研究價值,研究成果被廣泛用于航空航天、軍事、工業(yè)、氣象、交通以及文物保護等領域。近年來,隨著消費級深度傳感器的普及,利用RGBD序列估計3D場景流逐漸成為計算機視覺研究領域的熱點問題。雖然現有的RGBD序列場景流計算方法能夠獲取較為準確的估計結果,但是當圖像序列包含復雜背景和多個運動目標時,由于現有方法通常采用人工設定深度圖像初始分層層數并且得到的初始場景分割圖中只包含深度信息,導致并不能完全準確地分割獨立運動目標,致使場景流估計效果較差。針對以上問題,本文主要研究基于深度自動分層的RGBD序列3D場景流計算技術,主要研究工作包括:1.首先對3D場景流計算技術的研究背景和現狀進行了介紹,然后論述了圖像光流與3D場景流的對應關系,并對3D場景流技術的兩類主要方法進行了重點分析。2.針對現有3D場景流計算方法在復雜場景下估計效果較差的問題,提出基于光流的深度圖像自動分層與分割優(yōu)化方法。首先設定任意初始分層層數,然后利用K均值聚類計算深度圖像初始分割結果,再根據RGB圖像序列光流估計結果分別判斷、合并相鄰層,最終獲取深度圖像的分層與分割結果。相對于傳統(tǒng)的人工分層方法,本文方法不僅能夠實現自動分層,而且得到的分割結果是與運動目標相關的,將各個目標獨立分層,更利于分層場景流的計算。3.將本文深度圖像自動分層和分割優(yōu)化方法應用在RGBD圖像序列分層場景流的計算當中,介紹了其對應的能量泛函,然后詳細論述了各個約束項,最后詳細介紹了本文方法應用在分層場景流計算中的求解過程。4.采用Middlebury 2003測試圖像集、Middlebury 2005測試圖像集、SRSF真實場景圖像集、RGBD跟蹤場景圖像集測試本文深度圖像自動分層與分割優(yōu)化在分層場景流計算中的應用效果,同時進一步測試本文方法的有效性。實驗結果表明:1)本文分層場景流計算方法實現了自動分層,不需要人工選擇分層數以得到最佳分層效果,并且最終得到的場景分割效果更加準確,能夠準確的將各運動目標和背景獨立分層;2)本文方法計算得到的場景流計算誤差更小,最終得到的場景流計算結果也更符合場景的真實3D運動。
[Abstract]:3D scene flow is a three-dimensional motion vector field of space scene or object motion. It contains 3D motion and structure information of scene or object, and can be used in target motion estimation and tracking, attitude recognition, autonomous obstacle avoidance, etc. The research direction of path planning has important research value, and the research results are widely used in aerospace, military, industry, meteorology, traffic and heritage conservation and other fields. In recent years, with the popularity of consumer-level depth sensors, using RGBD sequences to estimate 3D scene flow has gradually become a hot issue in the field of computer vision. Although the existing RGBD sequence scene flow calculation method can obtain more accurate estimation results, but when the image sequence contains complex background and multiple moving targets, Because the existing methods usually use manual to set the initial layer number of depth image and only contain depth information in the initial scene segmentation image, it is not possible to segment the independent moving object completely and accurately, so the effect of scene flow estimation is poor. Aiming at the above problems, this paper mainly studies the RGBD sequence 3D scene flow computing technology based on depth automatic stratification, the main research work includes: 1. Firstly, the research background and present situation of 3D scene flow computing technology are introduced, then the corresponding relationship between image optical flow and 3D scene flow is discussed, and the two main methods of 3D scene flow technology are analyzed emphatically. In order to solve the problem that the existing 3D scene flow estimation methods have poor performance in the estimation of complex scenes, an optical flow-based method for automatic delamination and segmentation optimization of depth images is proposed. First, the number of arbitrary initial layers is set, then the initial segmentation results of depth images are calculated by K-means clustering, and then judged according to the optical flow estimation results of RGB image sequence, the adjacent layers are merged, and the results of delamination and segmentation of depth images are obtained. Compared with the traditional artificial stratification method, this method can not only achieve automatic stratification, but also the segmentation results are related to moving objects. In this paper, the method of automatic stratification and segmentation optimization of depth image is applied to the calculation of hierarchical scene flow of RGBD image sequence, and its corresponding energy functional is introduced, and each constraint item is discussed in detail. Finally, the solution process of the method applied in hierarchical scene flow calculation is introduced in detail. 4. 4. Using Middlebury 2003 test image set and Middlebury 2005 test image set to test the real scene image set RGBD tracking scene image set to test the application effect of the depth image automatic stratification and segmentation optimization in hierarchical scene flow calculation. At the same time, the effectiveness of this method is further tested. The experimental results show that the hierarchical scene flow calculation method in this paper realizes automatic stratification, does not need to manually select the number of layers to get the best result of stratification, and the final result of scene segmentation is more accurate. Each moving object and background can be accurately stratified. (2) the calculation error of scene flow obtained by this method is smaller, and the result of scene flow calculation is more consistent with the real 3D motion of the scene.
【學位授予單位】：南昌航空大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

【參考文獻】

相關期刊論文 前1條

1 鮮斌;劉洋;張旭;曹美會;;基于視覺的小型四旋翼無人機自主飛行控制[J];機械工程學報;2015年09期

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本文編號：1780907