Novelty Detection for Person Re-identification in an OpenWorld

A fundamental assumption in most contemporary person re-identification research, is that all query persons
that need to be re-identified belong to a closed gallery of known persons, i.e., they have been observed and a
representation of their appearance is available. For several real-world applications, this closed-world assumption
does not hold, as image queries may contain people that the re-identification system has never observed
before. In this work, we remove this constraining assumption. To do so, we introduce a novelty detection
mechanism that decides whether a person in a query image exists in the gallery. The re-identification of persons
existing in the gallery is easily achieved based on the persons representation employed by the novelty
detection mechanism. The proposed method operates on a hybrid person descriptor that consists of both supervised
(learnt) and unsupervised (hand-crafted) components. A series of experiments on public, state of the art
datasets and in comparison with state of the art methods shows that the proposed approach is very accurate in
identifying persons that have not been observed before and that this has a positive impact on re-identification
accuracy.