Key Person Aided Re-identification in Partially Ordered Pedestrian Set
This work addresses person re-identification for surveillance applications, offering a novel approach that is incremental in its use of temporal ordering.
The paper tackles the challenge of person re-identification by proposing a key person aided framework that leverages outstanding individuals to improve matching, achieving improved accuracy across all ranks on two video datasets.
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.