标题: | 以人类为基础的视讯处理及其在监控上的应用 Human-based Video Processing and its Application to Surveillance |
作者: | 赖育骏 Yu-Chun Lai 廖弘源 Hong-Yuan Mark Liao 资讯科学与工程研究所 |
关键字: | 以人类为基础的视讯处理;场景分割;PTZ摄影机;线性生产规划赛局;人类动作辨识;监控系统;Human-based video processing;Scene segmentation;PTZ camera;Linear Production Game;Human motion recognition;Surveillance system |
公开日期: | 2011 |
摘要: | 近年来以人类为基础的视讯处里一直是相当热门的研究题目,主要原因为人类通常是影片中的拍摄对象,例如,电影,监视器影片,以及运动影片。因此,如果可以在视讯影片中针对人类的部分加以处理,对于视讯内容的分析会相当的有帮助。常见的人类为基础的视讯处理包含了人类侦测,切割,以及动作辨识等技术。并且可分为针对储存影片的off-line 处理以及针对即时环境的on-line 处理。在本论文中,我们提出以人类为基础的视讯处理技术并且将其应用在智慧型监控系统上。在第一项的研究中,我们针对已经录制完毕的影片,提出了以背景资讯为基础的场景分割方法。我们利用Mosaic 的技术将属于前景部分(通常是人物的部分)的资讯移除并且试着重建被遮蔽的背景。接着根据背景的资讯取出低阶的视觉特征来估测影片中两shot 之间的相似程度。并且参考电影制作的法则将shot 群组化找出影片中不同场景之间的边界位置。在找寻出场景边界位置之后,可以简化后续的视讯分析工作。在第二项的研究主题中,我们针对即时的监控系统提出我们的主动式摄影机网路动作规划技术。主动式摄影机可以拉近来观测目标,可以提供较清楚的影像,因此非常符合智慧型监控系统上的需求。因此,我们提出一个线性生产规划赛局(Linear Production Game)解法来控制摄影机网路中主动式Pan Tilt Zoom 摄影机的参数。我们提出的非线性函式可以更加有效的摄影机去追踪多个观测目标,并且经由参数的拓展以及加上新的线性限制条件,可以转换为一个线性生产规划赛局(Linear Production Game)。由于线性生产规划赛局可以在多项式时间内求得最佳解,因此我们提出的方法相当有效率以及精确。在第三项的研究主题中,我们针对人类动作辨识问题提出一个以局部特征为基础的辨识技术。我们根据局部特征的表示法提出一个人类动作辨识的架构。两种不同的局部特征,包含动作的长期趋势以及短期外型变化分别被抽取出来用来描述人类动作。最后经由adaboost 的学习方法取出具有鉴别力的局部特征组合来辨识人类动作。 In recent years, human-based video processing has attracted a great deal of attention in the field of computer vision. This is because human usually is the major subject in a video such as movie, surveillance video, and sport video. Therefore, a videovprocessing technique based on human can provide rich information for video content analysis. Generally, common human-based video processing includes human detection, human segmentation, human motion recognition, and so on. Furthermore, according to the real-time requirements of an application, it can be categorized to the off-line processing for a video storage and the on-line processing for a real-time environment. In this dissertation, we put our emphasis on the human-based video processing and apply these techniques to an intelligent surveillance application. In the first topic, we propose a scene segmentation approach based on the analysis of background information for the off-line processing. The mosaic technique is utilized to remove the foreground parts (human) and reconstruct the occluded background. According to the background information, several low-level visual features are integrated to compute the similarity measure between two shots; moreover, the rules of film-making are used to guide the shot grouping process. After the boundaries among different scenes are detected, the following video analysis rocessing can be simplified. In the second topic, we proposed an active camera network reconfiguration technique for an on-line surveillance system. Since an active camera (for example, a pan, tilt, zoom camera) be able to fixate a human subject to obtain a large view of people, it is suitable for intelligent surveillance system. Therefore, a camera network reconfiguration solution is proposed to adjust pan, tilt, and zoom parameters in a PTZ camera network for video surveillance application. The non-linear objective function we proposed better utilizes a network's cameras to track multiple targets. We also show that, by expanding the unknown parameters and imposing new constraints, the non-linear objective function can be converted into a linear production game (LPG) problem. Since an LPG yields an optimal solution that can be evaluated in polynomial time, the proposed method is efficient and accurate. In our third work, a human motion recognition framework based on local feature representation is proposed. A clay based feature to describe long-term movement trend and a motion history image (MHI) based feature to describe short-term shape variation, are extracted respectively. Then, the AdaBoost approach is applied to select a best feature set for discriminating the human motions. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT079317829 http://hdl.handle.net/11536/40553 |
显示于类别: | Thesis |
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