| Summary: | 博士 === 國立臺灣大學 === 資訊工程學研究所 === 97 === Three important research topics in visual surveillance are studied, including background modeling, holistic pedestrian detection, and part-based pedestrian detection. Most previous background modeling approaches are pixel-based, while some approaches began to study block-based representations which are more robust to non-stationary backgrounds. We propose a method that integrates block- and pixel-based approaches into a single framework. Quantitative results show that the proposed method has better classification results than existing single-level approaches. In addition, we develop a method that can detect holistic pedestrians in images. In our approach, heterogeneous features are employed for weak-learner selection, and a novel cascaded structure that exploits both the stage-wise classification information and the inter-stage cross-reference information is proposed. Experiment results show that our approach can detect pedestrians with both efficiency and accuracy. We also propose a multi-class multi-instance boosting method for effective part-based pedestrian detection in images. Training examples are represented as a set of non-aligned instances, and the alignment problem caused by human appearance variation can be handled. Our method has the feature-sharing ability in a cascaded structure for efficient detection. Experiment results demonstrate the superior performance of the proposed method. We also combine background modeling and pedestrian detection techniques for visual surveillance application.
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