Real-time Dynamic Background Segmentation Based on Multiple Reference Value Model
| Autor: | Jian-Wen Peng, 彭建文 |
|---|---|
| Rok vydání: | 2007 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 95 In this thesis, we proposed a reliable and precise method for building and maintaining the reference background of a detected environment. Background segmentation (sub-traction) plays an important role in video surveillance systems and related applications. In order to extract the specific targets, applications must recognize what are objects (foreground) and what are not. Therefore, the fist step in such systems is usually to build a reference background model for the detected scene, and then the foreground can be extracted by comparing with the reference background model. In the existing literature, each pixel of a reference background model has only one reference value to the real background of the detected scene. However, each pixel in the proposed multiple reference value (MRV) background model may have multiple reference values. Thus, even in complex or disorder scenes, reference backgrounds can also be correctly built. Updating reference background is another important step for background segmen-tation. Because the detected scene will be changed, such as moving shadows of clouds or buildings, the reference background model must be modified to reflect these varia-tions. Otherwise, such applications will result in erroneous foreground segmentations. However, the situations of causing erroneous foreground segmentations are seldom discussed. In this thesis, a global update and a local update methods are employed as the strategies for a reference background update; they control the entire and partial modi-fication of a reference background model, respectively. Therefore, the reference back-ground model can be used more robust than other proposed models for a long period of surveillance. In addition, the situations of causing erroneous foreground segmentations are also discussed in details. By experimental results, the proposed method can obtain a more precise reference background model and preserves more details of a segmented foreground. Moreover, because of the reliable update strategies, the system can operate normally at daytime and nighttime. In addition, the system also can resist camera and object shaking. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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