Re-identification with multiple source-cameras

Abstract

Target re-identification approaches generally perform association between camera pairs only. However, in a multicamera system many-to-one camera associations are needed when targets transit from multiple source-cameras to a destinationcamera. To address this problem, we propose a person reidentification approach that generates camera-invariant object matching scores, which are based on re-identification score variations in multiple camera pairs. Each camera pair is represented with two parametric distribution models obtained by curve fitting on intra-class and inter-class target matching scores. These two models are combined to generate the likelihood of a correct match between a new target in the destination-camera and those in all source-cameras. We show the improvement in the performance of the proposed re-identification approach compared to existing pairwise approaches on two publicly available datasets.