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2016 | OriginalPaper | Buchkapitel

Human-in-the-Loop Person Re-identification

verfasst von : Hanxiao Wang, Shaogang Gong, Xiatian Zhu, Tao Xiang

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

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.

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Fußnoten
1
In a large size gallery set, true matches are often scarce (only one-shot) and overwhelmed (appear in low-ranks) by false matches of high-ranks in the rank list.
 
2
No limitation on considering any distance/similarity metrics, either learned or not.
 
3
A different 26,960-dim LOMO feature [11] were used for the published XQDA and MLAPG results [11, 12] shown in Table 3. They were worsened using the 5,138-dim feature [47] adopted in our experiments, not shown here due to space limitation.
 
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Metadaten
Titel
Human-in-the-Loop Person Re-identification
verfasst von
Hanxiao Wang
Shaogang Gong
Xiatian Zhu
Tao Xiang
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-46493-0_25