  Occluded person re-identification (Re-ID) aims at addressing the occlusion
problem when retrieving the person of interest across multiple cameras. With
the promotion of deep learning technology and the increasing demand for
intelligent video surveillance, the frequent occlusion in real-world
applications has made occluded person Re-ID draw considerable interest from
researchers. A large number of occluded person Re-ID methods have been proposed
while there are few surveys that focus on occlusion. To fill this gap and help
boost future research, this paper provides a systematic survey of occluded
person Re-ID. Through an in-depth analysis of the occlusion in person Re-ID,
most existing methods are found to only consider part of the problems brought
by occlusion. Therefore, we review occlusion-related person Re-ID methods from
the perspective of issues and solutions. We summarize four issues caused by
occlusion in person Re-ID, i.e., position misalignment, scale misalignment,
noisy information, and missing information. The occlusion-related methods
addressing different issues are then categorized and introduced accordingly.
After that, we summarize and compare the performance of recent occluded person
Re-ID methods on four popular datasets: Partial-ReID, Partial-iLIDS,
Occluded-ReID, and Occluded-DukeMTMC. Finally, we provide insights on promising
future research directions.
