Add-On Strategies for Fine-Grained Pedestrian Classification

In this paper, we present four add-on strategies for the fine-grained pedestrian classification task. These strategies are: (1) super-resolution based image preprocessing, which helps to recover the image details; (2) patch dividing based deep feature extraction, which extracts features in a way that preserves the spatial layout of input images; (3) pose- wise classifier sharing, which learns robust classifiers and makes robust predictions using pose information; and (4) graphical model based inference, which utilizes the interdependence between different subcategories to update raw estimations. The proposed strategies are independent and flexible, which make it easy to implement them in practice. We evaluated these strategies on the CRP dataset and confirmed that all of them lead to improvements over the baseline. We also confirmed an improvement over the state-of-the-art when all strategies are combined together.