Person re-identification (ReID) aims at finding the same person in different
cameras. Training such systems usually requires a large amount of cross-camera
pedestrians to be annotated from surveillance videos, which is labor-consuming
especially when the number of cameras is large. Differently, this paper
investigates ReID in an unexplored single-camera-training (SCT) setting, where
each person in the training set appears in only one camera. To the best of our
knowledge, this setting was never studied before. SCT enjoys the advantage of
low-cost data collection and annotation, and thus eases ReID systems to be
trained in a brand new environment. However, it raises major challenges due to
the lack of cross-camera person occurrences, which conventional approaches
heavily rely on to extract discriminative features. The key to dealing with the
challenges in the SCT setting lies in designing an effective mechanism to
complement cross-camera annotation. We start with a regular deep network for
feature extraction, upon which we propose a novel loss function named
multi-camera negative loss (MCNL). This is a metric learning loss motivated by
probability, suggesting that in a multi-camera system, one image is more likely
to be closer to the most similar negative sample in other cameras than to the
most similar negative sample in the same camera. In experiments, MCNL
significantly boosts ReID accuracy in the SCT setting, which paves the way of
fast deployment of ReID systems with good performance on new target scenes.

本文研究了单摄像机训练下的人物再识别，提出了一种新的损失函数 MCNL，利用概率来解决单摄像机下缺乏跨摄像机信息的问题，实验表明 MCNL 有效提升了 ReID 的准确率。