Local Feature Based Person Reidentification in Infrared Image Sequences

In this paper, we address the task of appearance based person reidentification in infrared image sequences. While common approaches for appearance based person reidentification in the visible spectrum acquire color histograms of a person, this technique is not applicable in infrared for obvious reasons. To tackle the more difficult problem of person reidentification in infrared, we introduce an approach that relies on local image features only and thus is completely independent of sensor specific features which might be available only in the visible spectrum. Our approach fits into an Implicit Shape Model (ISM) based person detection and tracking strategy described in previous work. Local features collected during tracking are employed for person reidentification while the generalizing appearance codebook used for person detection serves as structuring element to generate person signatures. By this, we gain an integrated approach that allows for fast online model generation, a compact representation, and fast model matching. Since the model allows for a joined representation of appearance and spatial information, no complex representation models like graph structures are needed. We evaluate our person reidentification approach on a subset of the CASIA infrared dataset.