Person re-identification via efficient inference in fully connected CRF

TL;DR

This paper proposes an efficient inference method using a fully connected CRF model for person re-identification, improving accuracy by considering both probe-gallery and gallery-gallery similarities.

Contribution

It introduces a novel fully connected CRF model with an efficient inference algorithm tailored for person re-identification tasks.

Findings

Outperforms existing methods on public datasets

Effectively handles appearance variations and occlusions

Demonstrates computational efficiency in dense CRF inference

Abstract

In this paper, we address the problem of person re-identification problem, i.e., retrieving instances from gallery which are generated by the same person as the given probe image. This is very challenging because the person's appearance usually undergoes significant variations due to changes in illumination, camera angle and view, background clutter, and occlusion over the camera network. In this paper, we assume that the matched gallery images should not only be similar to the probe, but also be similar to each other, under suitable metric. We express this assumption with a fully connected CRF model in which each node corresponds to a gallery and every pair of nodes are connected by an edge. A label variable is associated with each node to indicate whether the corresponding image is from target person. We define unary potential for each node using existing feature calculation and matching techniques, which reflect the similarity between probe and gallery image, and define pairwise potential for each edge in terms of a weighed combination of Gaussian kernels, which encode appearance similarity between pair of gallery images. The specific form of pairwise potential allows us to exploit an efficient inference algorithm to calculate the marginal distribution of each label variable for this dense connected CRF. We show the superiority of our method by applying it to public datasets and comparing with the state of the art.