Key Person Aided Re-identification in Partially Ordered Pedestrian Set

TL;DR

This paper introduces a key person aided framework for person re-identification that leverages outstanding individuals and temporal ordering to improve matching accuracy across different camera views.

Contribution

It proposes a novel approach using key persons and partial order based on entry time to enhance re-identification performance.

Findings

Outperforms state-of-the-art methods in experiments.

Significantly improves matching accuracy at all ranks.

Utilizes saliency measurement for key person selection.

Abstract

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.