Open-set Person Re-identification

TL;DR

This paper introduces the open-set person re-identification problem, addressing real-world challenges where the probe individual may not be in the gallery, and provides a new dataset and benchmark for evaluation.

Contribution

It defines the open-set re-identification problem, creates a new surveillance-based dataset, and establishes a benchmark protocol for future research.

Findings

Baseline algorithms perform poorly on open-set scenarios

Open-set re-identification remains largely unresolved

Further research is needed for practical applications

Abstract

Person re-identification is becoming a hot research for developing both machine learning algorithms and video surveillance applications. The task of person re-identification is to determine which person in a gallery has the same identity to a probe image. This task basically assumes that the subject of the probe image belongs to the gallery, that is, the gallery contains this person. However, in practical applications such as searching a suspect in a video, this assumption is usually not true. In this paper, we consider the open-set person re-identification problem, which includes two sub-tasks, detection and identification. The detection sub-task is to determine the presence of the probe subject in the gallery, and the identification sub-task is to determine which person in the gallery has the same identity as the accepted probe. We present a database collected from a video surveillance setting of 6 cameras, with 200 persons and 7,413 images segmented. Based on this database, we develop a benchmark protocol for evaluating the performance under the open-set person re-identification scenario. Several popular metric learning algorithms for person re-identification have been evaluated as baselines. From the baseline performance, we observe that the open-set person re-identification problem is still largely unresolved, thus further attention and effort is needed.