Person Re-Identification by Discriminative Selection in Video Ranking

TL;DR

This paper introduces a novel video-based person re-identification model that automatically selects discriminative video fragments to improve identification accuracy in surveillance scenarios, outperforming existing methods.

Contribution

The work proposes an automatic discriminative fragment selection method combined with a video ranking function for enhanced person ReID, addressing limitations of single-frame approaches.

Findings

Outperforms state-of-the-art ReID methods on multiple datasets

Effectively handles noisy and incomplete video sequences

Demonstrates the advantage of space-time features over single-frame features

Abstract

Current person re-identification (ReID) methods typically rely on single-frame imagery features, whilst ignoring space-time information from image sequences often available in the practical surveillance scenarios. Single-frame (single-shot) based visual appearance matching is inherently limited for person ReID in public spaces due to the challenging visual ambiguity and uncertainty arising from non-overlapping camera views where viewing condition changes can cause significant people appearance variations. In this work, we present a novel model to automatically select the most discriminative video fragments from noisy/incomplete image sequences of people from which reliable space-time and appearance features can be computed, whilst simultaneously learning a video ranking function for person ReID. Using the PRID$2011$, iLIDS-VID, and HDA+ image sequence datasets, we extensively conducted comparative evaluations to demonstrate the advantages of the proposed model over contemporary gait recognition, holistic image sequence matching and state-of-the-art single-/multi-shot ReID methods.