Abstract

Abstract. Person re-identification is becoming a hot research for devel-oping both machine learning algorithms and video surveillance applica-tions. 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 as-sumes 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 col-lected from a video surveillance setting of 6 cameras, with 200 persons and 7,413 images segmented. Based on this database, we develop a bench-mark 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 base-line performance, we observe that the open-set person re-identification problem is still largely unresolved, thus further attention and effort is needed.