Framelet features for pedestrian detection in noisy depth images

Pedestrian detection based on the framelet features in noisy depth images is investigated in this paper. For capturing the local features and attenuating the effects of noise in depth images, a features optimization model is proposed to adaptively select the framelet features for classification. The selected framelet features extracted by the model and SVM with a linear kernel is adopted as the feature and classifier, respectively. The proposed framelet features under a tight and redundant system can preserve the shape information while reducing the impact of noise. Experimental results also show that the proposed method based on framelet features can achieve a great improvement in noisy depth images, and the improvement is over one order of magnitude than HDD and HOG.