Hand-Crafted vs Deep Features: A Quantitative Study of Pedestrian Appearance Model

We propose a deep discriminative appearance model (DDAM) based on convolutional neural network (CNN) for pedestrians. The training stage of our supervised D-DAM model does not depend on a large amount of data. In our model, we introduce a progressive batch refinement technique to fine tune the CNN for modeling the appearance of the pedestrian. After fine-tuning, the model achieves 98% accuracy for pedestrian and non-pedestrian classification. Moreover, we also introduce a novel discrimination index (DI) for evaluating the spatio-temporal discrimination effectiveness of both hand-crafted and deep features. We perform experiments on pre-trained CNN model, our D-DAM model, and 3 baseline hand-crafted features including HoG, LBP, and Color histogram. The results show that our D-DAM model achieves higher classification accuracy and better spatio-temporal discrimination ability compared to all the hand-crafted features.