Ideally person re-identification seeks for perfect feature representation and
metric model that re-identify all various pedestrians well in non-overlapping
views at different locations with different camera configurations, which is
very challenging. However, in most pedestrian sets, there always are some
outstanding persons who are relatively easy to re-identify...
Inspired by the
existence of such data division, we propose a novel key person aided person
re-identification framework based on the re-defined partially ordered
pedestrian sets. The outstanding persons, namely "key persons", are selected by
the K-nearest neighbor based saliency measurement. The partial order defined by
pedestrian entering time in surveillance associates the key persons with the
query person temporally and helps to locate the possible candidates. Experiments conducted on two video datasets show that the proposed key person
aided framework outperforms the state-of-the-art methods and improves the
matching accuracy greatly at all ranks.