Human-based Video Processing and its Application to Surveillance
| Autor: | Yu-Chun Lai, 賴育駿 |
|---|---|
| Rok vydání: | 2012 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 100 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. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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