Computer Vision Techniques for Effective Pedestrian Detection
Date Issued
2009
Date
2009
Author(s)
Chen, Yu-Ting
Abstract
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.
Subjects
Hierarchical background modeling
contrast histogram
human detection
holistic pedestrian detection
cascaded feed-forward classifiers
meta-stages
part-based pedestrian detection
multi-instance learning
multi-class multi-instance boosting
feature sharing
visual surveillance
Type
thesis
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