Action Recognition under Occusions Using Sparse Coding
| Autor: | Kai-Ting Chuang, 莊凱婷 |
|---|---|
| Rok vydání: | 2012 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 100 Recently, behavior analysis has become an important task in computer vision, and is expected to enable many applications, such as intelligent video retrieval, human interaction system, and so on. Most existing visual behavior researches put focus on analyzing single behavior. Therefore, we present a general model for several highlighted daily behaviors (e.g. greeting, shaking hands, and walking), which works efficiently and effectively although occlusions occur. In this thesis, we based on sparse representation to solve the problem of human behavior recognition in video. For each behavior, a overcomplete dictionary is solving the sparse optimization problems. Through the learning of every behavior dictionary, each dictionary can effectively represent a specific behavior. The proposed system consists of three components: foreground region extraction, feature extraction, and Sparse Coding to behavior analysis. First of all, foreground objects and people detection information are extracted by using Gaussian mixture model and Connected Component. Second, features are extracted by using R Transform and Histogram of Oriented Gradients. Finally, we employ a Sparse Representation-based Classification and Hamming Distance Classification to infer the problem of behavior analysis. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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