Human-In-The-Loop Person Re-Identification
This addresses the problem of scaling person re-identification to real-world camera networks with large galleries, offering a practical solution for surveillance and security applications, though it is incremental in its approach.
The paper tackles the scalability issues in person re-identification by proposing a human-in-the-loop model that eliminates the need for pre-labelled training data and adapts to large gallery sizes through incremental learning from human feedback, achieving instant improvement in re-id ranking.
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.