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• W2791286014 abstract "One of the goals of person re-identification systems is to support video-surveillance operators and forensic investigators to find an individual of interest in videos acquired by a network of non-overlapping cameras. This is attained by sorting images of previously observed individuals for decreasing values of their similarity with a given probe individual. Existing appearance descriptors, together with their similarity measures, are mostly aimed at improving ranking quality. The authors address instead the issue of processing time, which is also relevant in practical applications involving interaction with human operators. They show how a trade-off between processing time and ranking quality, for any given descriptor, can be achieved through a multi-stage ranking approach inspired by multi-stage classification approaches, which they adapt to the re-identification ranking task. The authors analytically model the processing time of multi-stage system and discuss the corresponding accuracy, and derive from these results practical design guidelines. They then empirically evaluate their approach on three benchmark data sets and four state-of-the-art descriptors." @default.
• W2791286014 created "2018-03-29" @default.
• W2791286014 creator A5065359946 @default.
• W2791286014 creator A5085227924 @default.
• W2791286014 date "2018-02-14" @default.
• W2791286014 modified "2023-09-25" @default.
• W2791286014 title "Multi‐stage ranking approach for fast person re‐identification" @default.
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• W2791286014 doi "https://doi.org/10.1049/iet-cvi.2017.0240" @default.
• W2791286014 hasPublicationYear "2018" @default.
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