| Author: | Lun, Tak-chuen |
| Title: | Techniques for video segmentation and object tracking |
| Degree: | M.Sc. |
| Year: | 2001 |
| Subject: | Image processing Hong Kong Polytechnic University -- Dissertations |
| Department: | Multi-disciplinary Studies Department of Electronic and Information Engineering |
| Pages: | 87 leaves : ill. ; 30 cm |
| Language: | English |
| Abstract: | To take advantage of the potential new applications created by the emerging MPEG-4 and MPEG-7 object-based coding and indexing standards, video content must be structured as a composition of objects, for which a number of features are available. In some cases, these data are made available by the visual information production process itself. In other cases, this type of information is only partly available and thus some video analysis must be performed. Video analysis usually consists of identifying the relevant objects that-compose a scene (segmentation), and extracting relevant features for the individual objects or for the composed scene. The analysed data can be used both for content-based video coding and indexing. To provide the requested content-based functionalities, analysed data should be consistent in time, guaranteeing a correct tracking of the segmentation partition labels, and an appropriate handling of the extracted features. Two potentially useful video analysis results are: segmentation of the scene, and tracking of objects along the sequence. In this work, different techniques for video segmentation are first studied. The characteristics and constrains of different segmentation methods are investigated. A new block based recursive shortest spanning tree (BRSST) method is developed for object-based video segmentation. An application of the proposed techniques in car occupant detection is discussed. One of the most common applications of object-based video segmentation is object tracking. A software platform is then developed to study different techniques for object tracking. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b15995811.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.58 MB | Adobe PDF | View/Open |
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