Supervised Person Re-ID based on Deep Hand-crafted and CNN Features

Gaussian Fisher Vector (GFV) encoding is an extension of the conventional Fisher Vector (FV) that effectively discards the noisy background information by localizing the pedestrian position in the image. Nevertheless, GFV can only provide a shallow description of the pedestrian features. In order to capture more complex structural information, we propose in this paper a layered extension of GFV that we called LGFV. The representation is based on two nested layers that hierarchically refine the FV encoding from one layer to the next by integrating more spatial neighborhood information. Besides, we present in this paper a new rich multi-level semantic pedestrian representation built simultaneously upon complementary deep hand-crafted and deep Convolutional Neural Network (CNN) features. The deep hand-crafted feature is depicted by the combination of GFV mid-level features and high-level LGFV ones while a deep CNN feature is obtained by learning in a classification mode an effective embedding of the raw pedestrian pixels. The proposed deep hand-crafted features produce competitive accuracy with respect to the deep CNN ones without needing neither pre-training nor data augmentation, and the proposed multi-level representation further boosts the re-ID performance.