Supervised person re-identification assumes that a person has images captured
under multiple cameras. However when cameras are placed in distance, a person
rarely appears in more than one camera. This paper thus studies person re-ID
under such isolated camera supervised (ISCS) setting. Instead of trying to
generate fake cross-camera features like previous methods, we explore a novel
perspective by making efficient use of the variation in training data. Under
ISCS setting, a person only has limited images from a single camera, so the
camera bias becomes a critical issue confounding ID discrimination.
Cross-camera images are prone to being recognized as different IDs simply by
camera style. To eliminate the confounding effect of camera bias, we propose to
learn both intra- and inter-camera invariance under a unified framework. First,
we construct style-consistent environments via clustering, and perform
prototypical contrastive learning within each environment. Meanwhile, strongly
augmented images are contrasted with original prototypes to enforce
intra-camera augmentation invariance. For inter-camera invariance, we further
design a much improved variant of multi-camera negative loss that optimizes the
distance of multi-level negatives. The resulting model learns to be invariant
to both subtle and severe style variation within and cross-camera. On multiple
benchmarks, we conduct extensive experiments and validate the effectiveness and
superiority of the proposed method. Code will be available at
this https URL

在单独相机监督的设置下，通过构建样式一致的环境、在每个环境中进行原型对比学习以消除相机偏差和进行跨相机内外不变性设计，本研究提出的方法有效地解决了人员再识别中的关键问题，并在多个基准测试中进行了验证。

孤立摄像头监督的人员重新识别中实现摄像头内外不变性的学习

Learning Intra and Inter-Camera Invariance for Isolated Camera  Supervised Person Re-identification

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。