Toward Resolution-Invariant Person Reidentification via Projective Dictionary Learning.

Abstract

Person reidentification (ReID) has recently been widely investigated for its vital role in surveillance and forensics applications. This paper addresses the low-resolution (LR) person ReID problem, which is of great practical meaning because pedestrians are often captured in LRs by surveillance cameras. Existing methods cope with this problem via some complicated and time-consuming strategies, making them less favorable, in practice, and meanwhile, their performances are far from satisfactory. Instead, we solve this problem by developing a discriminative semicoupled projective dictionary learning (DSPDL) model, which adopts the efficient projective dictionary learning strategy, and jointly learns a pair of dictionaries and a mapping function to model the correspondence of the cross-view data. A parameterless cross-view graph regularizer incorporating both positive and negative pair information is designed to enhance the discriminability of the dictionaries. Another weakness of existing approaches to this problem is that they are only applicable for the scenario where the cross-camera image sets have a globally uniform resolution gap. This fact undermines their practicality because the resolution gaps between cross-camera images often vary person by person in practice. To overcome this hurdle, we extend the proposed DSPDL model to the variational resolution gap scenario, basically by learning multiple pairs of dictionaries and multiple mapping functions. A novel technique is proposed to rerank and fuse the results obtained from all dictionary pairs. Experiments on five public data sets show the proposed method achieves superior performances to the state-of-the-art ones.