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Erschienen in:
Improving Human Action Recognition Using Score Distribution and Ranking Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Query Based Adaptive Re-ranking for Person Re-identification

verfasst von : Andy Jinhua Ma, Ping Li

Erschienen in: Computer Vision -- ACCV 2014

Verlag: Springer International Publishing

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Abstract

Existing algorithms for person re-identification hardly model query variations across non-overlapping cameras. In this paper, we propose a query based adaptive re-ranking method to address this important issue. In our work, negative image pairs can be easily generated for each query under non-overlapping cameras. To infer query variations across cameras, nearest neighbors of the query positive match under two camera views are approximated and selected from positive matches in the training set. Locality preserving projections (LPP) are employed to ensure that each feature vector under one camera shares similar neighborhood structure with the corresponding positive match. Using existing re-identification algorithms as base score function, the optimal adaptive model is learnt by least-square regression with manifold regularization. Experimental results show that the proposed method can improve the ranking performance and outperforms other adaptive methods.

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Vorheriges Kapitel Clustering Ensemble Tracking
Nächstes Kapitel Improved Color Patch Similarity Measure Based Weighted Median Filter
Fußnoten
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Metadaten
Titel
Query Based Adaptive Re-ranking for Person Re-identification
verfasst von
Andy Jinhua Ma
Ping Li
Copyright-Jahr
2015
Buch
Computer Vision -- ACCV 2014

Print ISBN: 978-3-319-16813-5

Electronic ISBN: 978-3-319-16814-2

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-16814-2_26

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