  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.
