Abstract
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.