Pedestrian classification by using stacked sparse autoencoders

Automated Pedestrian classification has been the topic of interest over past many years. Various traditional and neural network based approaches have been adopted to recognize pedestrians. In this paper, we employ stacked sparse autoencoder as a deep learning building block for object feature extraction. Salient feature maps of the input images are generated with the help of SLIC superpixel-based graph manifold ranking. These salient maps are then served as input to the stacked sparse autoencoder in the next step. Softmax regression is selected as the classifier to identify the pedestrians. The learning and testing procedures are performed with the publically available datasets. The experiments results obtained are compared with a recent traditional pedestrian method. The better performance rates depict the robustness of the proposed approach.