Local Feature Based Person Reidentification in Infrared Image Sequences

Abstract

In this paper, we address the task of appearance basedperson reidentification in infrared image sequences. Whilecommon approaches for appearance based person reidentificationin the visible spectrum acquire color histograms ofa person, this technique is not applicable in infrared for obviousreasons. To tackle the more difficult problem of personreidentification in infrared, we introduce an approachthat relies on local image features only and thus is completelyindependent of sensor specific features which mightbe available only in the visible spectrum. Our approachfits into an Implicit Shape Model (ISM) based person detectionand tracking strategy described in previous work.Local features collected during tracking are employed forperson reidentification while the generalizing appearancecodebook used for person detection serves as structuringelement to generate person signatures. By this, we gain anintegrated approach that allows for fast online model generation,a compact representation, and fast model matching.Since the model allows for a joined representation ofappearance and spatial information, no complex representationmodels like graph structures are needed. We evaluateour person reidentification approach on a subset of the CASIAinfrared dataset.