【作者】 周磊；

【导师】 徐钊； 华钢；

【作者基本信息】 中国矿业大学 ， 通信与信息系统， 2010， 博士

【摘要】 现有煤矿视频监控系统监控模式简单,只是实时采集和显示系统。随着视频头增加,在众多视频中发现异常十分困难,造成漏检率高,不能满足煤矿安全监控的要求,给煤矿安全生产埋下隐患。实现对煤矿监控视频的机器监视是煤矿视频监控系统发展的迫切要求。为了达到这个目的,首先需要检测煤矿目标。这一过程受到多种因素的制约,包括环境因素,技术理论与方法,以及实时性等方面。其中最为关键的就是缺少对煤矿领域特征和特点的总结,以至于缺乏针对煤矿的,行之有效的图像和视频处理算法。知觉编组能用最少的领域知识形成目标假设。当目标满足格式塔准则时,知觉编组可以降低视觉过程中目标检测和识别的计算复杂度。本文引入视觉感知系统信息处理理论中的知觉编组完成煤矿目标检测。本文分为四个部分阐述:(1)光照不均图像的编组种子提取方法;(2)交互式闭合轮廓知觉编组算法;(3)基于自底向上注意机制的知觉编组算法;(4)基于自顶向下注意机制的知觉编组算法。(1)编组种子提取是知觉编组过程中的重要一环,一个有效的编组种子提取算法将显著降低后续知觉编组的难度,而环境因素造成煤矿图像具有光照不均现象,影响知觉编组过程中编组种子提取的准确度。为了解除环境因素的制约,本文研究了煤矿光照不均图像的编组种子提取算法。首先说明光照不均图像的边缘模型,研究了两种基于视觉非线性特性的光照不均图像边缘提取算法,最后说明如何将粗糙,不光滑且相交的边缘轮廓平滑为编组种子。本文实验表明:基于视觉非线性特性提取的编组种子,减少了光照不均图像中亮区域的边缘冗余和暗区域边缘缺失现象。(2)在煤矿视频监控目标中,满足编组准则的目标可能有多个,此时需要多次执行编组过程,增加了运算时间,并且也未必能够检测到最终结果。本文在分析交互式和非交互式知觉编组算法异同的基础上,研究了一种基于双权重图的交互式闭合轮廓知觉编组算法。本文实验表明:交互式与非交互式知觉编组算法在算法复杂度上差距很小,但交互式编组算法的收敛速度是非交互式算法的3倍以上。(3)交互式知觉编组算法的初始条件如果单纯依靠人手动选择,必须由了解并熟悉煤矿监控领域的专门人员操作,这降低了算法的适应性。本文采用注意机制选择初始条件,代替交互过程,提出了基于自底向上注意机制和自顶向下注意机制的煤矿复杂图像知觉编组算法。本文实验表明:自底向上的注意计算模型在视频高兴趣区域提取任务中每秒可以运行4次以上,但是存在位置偏移现象,偏移率约40%。(4)基于自顶向下注意机制的知觉编组算法可以选择全局特征,边缘错误率和线索错误率都相对较低,在提取一条显著编组种子时,边缘错误率和线索错误率为0;随着编组种子增多,错误率逐步升高,在12条编组种子以内,两种错误率都低于40%。作者通过实验平台对本文提出的技术方法进行了实验,平台采用Matlab和C++实现。平台任务包括:编组种子提取,局部特征和全局特征模型化,最优化过程求解,以及两种注意机制的实现。该论文有图47幅,表4个,参考文献193篇。更多还原

【Abstract】 The surveillance pattern of current coal-mine video surveillance systems is too simple, they are just a system for real-time information collection and transmission. In fact, as the number of camera is increasing, it’s becoming more and more difficult to capture the abnormal status, that results in high omission factor, can’t satisfy the coal-mine demand for safety, so it’s a potential trouble for coal-mine safety production.To develop the coal-mine video surveillance system, it is necessary to apply the intelligent surveillance technique on coal-mine image and video processing. To achieve the purpose, first of all, we must detect objects out of the coal-mine videos and images. Perceptual grouping can form the object hypothesis with the least specific knowledge, and reduce the computation complexity of visual recognition. This dissertation introduces perceptual grouping of visual information processing system to detect the coal-mine objects.But the application of perceptual grouping algorithm is restricted by some factors, such as environment condition, convergence speed, and adaption. Aiming at the three problems, this dissertation is mainly organized by three parts: (1) Extraction of grouping seed for coal-mine uneven light images; (2) Interactive closed boundary grouping based on double-weight graph; (3) Perceptual grouping algorithm of coal-mine complex image based on bottom-up attention mechanism and perceptual grouping algorithm of coal-mine complex image based on up-bottom attention mechanism.(1) Grouping seed extraction, which will greatly reduce the difficulty of following perceptual grouping steps, is one important process of perceptual grouping. But the environment conditions result in uneven lighting phenomenon which disturbs the accuracy of grouping seed extraction of perceptual grouping. In order to unlock the restriction of environment conditions, this dissertation puts forward the grouping seed extraction algorithm for coal-mine uneven light images. Firstly, we show the edge model of uneven lighting image, and then based on nonlinear visual perception characteristic, we introduce two edge detection algorithms for uneven lighting image, at last, we explain how to transform these coarse unsmooth and intersecting edges to smooth grouping seeds. The experiments show that grouping seed extraction algorithm based on non-linear visual characteristic reduces the redundant edges at bright regions and loss of edges at dark regions.(2) The current perceptual grouping algorithms are mostly aiming at general application. At the coal-mine field, maybe there are many objects which fulfill the grouping laws, so we must run the perceptual grouping algorithm many times to focus on the final needed object, this process costs more computation time, and will not definitely find the wanted result. This dissertation introduces an interactive closed contour grouping algorithm based on double-weight graph, we analyze the similarities and differences between interactive and non-interactive perceptual grouping algorithms. The experiments show that: the computation complexity difference between the interactive and the non-interactive algorithms is small, but convergence speed of interactive algorithm is at least 3 times faster than that of non-interactive algorithm;(3) If the interactive process of interactive perceptual grouping algorithm completely depends on manual selection, we must ask for the professional surveillance worker to do it, this action reduces the flexibility of algorithm. Attention mechanism simulates the human’s visual process, directly locates around the most interesting object. This dissertation puts forward two attention mechanisms to subtitude the manual selection: one is based on bottom-up attention mechanism, the other is based on up- bottom attention mechanism. The experiments show that: the computation model of bottom-up attention mechanism run more than 4 times per second, but it has position deviation phenomenon which is about 40%. The perceptual grouping algorithm with specific knowledge based on up-bottom attention mechanism can select the global cue. Cue error ratio and seed error ratio are both low, when detecting only one seed, they both are zero, when below 12 seeds, they both are under the 40%.We have designed a platform implemented by Matlab and C++ for the experiment. The tasks of the platform include: grouping seed extraction, computation of global and local cues, solution of object function, realization of two kinds of attention mechanisms.The dissertation has 47 figures, 4 tables and 193 references.更多还原