Group Re-Identification via Unsupervised Transfer of Sparse Features Encoding

TL;DR

This paper introduces a novel group re-identification method that transfers knowledge from single person re-identification using sparse dictionary learning, effectively handling occlusions and positional changes in crowded scenarios.

Contribution

It proposes a new approach that leverages sparse dictionary transfer from single to group re-identification, improving robustness in crowded environments.

Findings

Outperforms state-of-the-art methods on i-LIDS and new datasets.

Effectively handles occlusions and positional variations within groups.

Demonstrates robustness in crowded scenarios.

Abstract

Person re-identification is best known as the problem of associating a single person that is observed from one or more disjoint cameras. The existing literature has mainly addressed such an issue, neglecting the fact that people usually move in groups, like in crowded scenarios. We believe that the additional information carried by neighboring individuals provides a relevant visual context that can be exploited to obtain a more robust match of single persons within the group. Despite this, re-identifying groups of people compound the common single person re-identification problems by introducing changes in the relative position of persons within the group and severe self-occlusions. In this paper, we propose a solution for group re-identification that grounds on transferring knowledge from single person re-identification to group re-identification by exploiting sparse dictionary learning. First, a dictionary of sparse atoms is learned using patches extracted from single person images. Then, the learned dictionary is exploited to obtain a sparsity-driven residual group representation, which is finally matched to perform the re-identification. Extensive experiments on the i-LIDS groups and two newly collected datasets show that the proposed solution outperforms state-of-the-art approaches.