A Benchmark for Vehicle Re-Identification in Mixed Visible and Infrared Domains

2024/01/01 by Qianqian Zhao, Simin Zhan, Rui Cheng +3
Chemistry · Engineering · #Infrared Target Detection Methodologies #Spectroscopy and Chemometric Analyses

paper · doi:10.1109/lsp.2024.3370492

crossref issued 2024/01/01 · crossref published 2024/01/01 · crossref published-print 2024/01/01 · openalex publication_date 2024/01/01 · crossref created 2024/02/27 · crossref deposited 2024/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/11 · crossref indexed 2026/07/29

Abstract

We propose a new benchmark for vehicle re-identification in mixed visible and infrared domains. Unlike cross-modal vehicle re-identification, we focus on a more realistic scenario, namely, mixed-modal vehicle re-identification, in which both probe and gallery sets contain visible and infrared images. We provide auto-cropped visible and infrared images, simulating data from actual surveillance systems. We design a mixed triplet loss function for model training. We report both mixed-modal and cross-modal retrieval performance. Our mixed method performs well in cross-modal retrieval, e.g., the Rank-1 identification rate is 79.19% in the visible-to-infrared retrieval mode.

Citations

• Deep Learning for Person Re-Identification: A Survey and Outlook
• BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

Cited by

• CLIPVehicle: A Unified Framework for Vision-based Vehicle Search

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