Hybrid Deep Architecture for Pedestrian Detection
碩士 === 國立清華大學 === 資訊工程學系 === 103 === In this thesis we propose a hybrid convolutional neural network (CNN)-classification Restricted Boltzmann Machine (ClassRBM) model for the task of pedestrian detection. Although deep-net approaches have been shown to be successful in tackling recognition and gene...
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| Format: | Others |
| Language: | en_US |
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2015
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| Online Access: | http://ndltd.ncl.edu.tw/handle/12098286776346291243 |
| Summary: | 碩士 === 國立清華大學 === 資訊工程學系 === 103 === In this thesis we propose a hybrid convolutional neural network (CNN)-classification Restricted Boltzmann Machine (ClassRBM) model for the task of pedestrian detection. Although deep-net approaches have been shown to be successful in tackling recognition and general object detection problems, its success in pedestrian detection is not clear and not competitive with the state-of-the-art feature pools plus boosted decision trees method. We integrate a fine-tuned AlexNet with a carefully-trained ClassRBM to achieve competitive performances in the INRIA and Caltech pedestrian dataset. The model jointly extracts local features and further processes them through multiple layers to extract high-level and global features. The top-layer ClassRBM performs inference from CNN features and outputs classification results as a probability distribution. An additional bounding-box regression with sampling method is employed for addressing the localization problem caused by low-quality region proposals. Our experiments demonstrate the successful results of deep net for pedestrian detection in many aspects.
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