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

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.