  Visible-infrared person re-identification (VI-ReID) aims to match persons
captured by visible and infrared cameras, allowing person retrieval and
tracking in 24-hour surveillance systems. Previous methods focus on learning
from cross-modality person images in different cameras. However, temporal
information and single-camera samples tend to be neglected. To crack this nut,
in this paper, we first contribute a large-scale VI-ReID dataset named
BUPTCampus. Different from most existing VI-ReID datasets, it 1) collects
tracklets instead of images to introduce rich temporal information, 2) contains
pixel-aligned cross-modality sample pairs for better modality-invariant
learning, 3) provides one auxiliary set to help enhance the optimization, in
which each identity only appears in a single camera. Based on our constructed
dataset, we present a two-stream framework as baseline and apply Generative
Adversarial Network (GAN) to narrow the gap between the two modalities. To
exploit the advantages introduced by the auxiliary set, we propose a curriculum
learning based strategy to jointly learn from both primary and auxiliary sets.
Moreover, we design a novel temporal k-reciprocal re-ranking method to refine
the ranking list with fine-grained temporal correlation cues. Experimental
results demonstrate the effectiveness of the proposed methods. We also
reproduce 9 state-of-the-art image-based and video-based VI-ReID methods on
BUPTCampus and our methods show substantial superiority to them. The codes and
dataset are available at: https://github.com/dyhBUPT/BUPTCampus.
