Current person re-identification (re-id) methods assume that (1) pre-labelled
training data are available for every camera pair, (2) the gallery size for
re-identification is moderate. Both assumptions scale poorly to real-world
applications when camera network size increases and gallery size becomes large.
Human verification of automatic model ranked re-id results becomes inevitable.
In this work, a novel human-in-the-loop re-id model based on Human Verification
Incremental Learning (HVIL) is formulated which does not require any
pre-labelled training data to learn a model, therefore readily scalable to new
camera pairs. This HVIL model learns cumulatively from human feedback to
provide instant improvement to re-id ranking of each probe on-the-fly enabling
the model scalable to large gallery sizes. We further formulate a Regularised
Metric Ensemble Learning (RMEL) model to combine a series of incrementally
learned HVIL models into a single ensemble model to be used when human feedback
becomes unavailable.

提出一种基于人机交互 (Human Verification Incremental Learning) 的人体再识别模型，将人类反馈信息累计到模型中，从而实现模型的标记无关性和可伸缩性，同时使用正则化的度量集成学习 (Regularised Metric Ensemble Learning) 模型，当没有人类反馈时将一系列增量学习的 HVIL 模型结合成单个集合模型，以处理大型库问题。