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Local Descriptors encoded by Fisher Vectors for
| Citations: | 20 - 0 self |
BibTeX
@MISC{Re-identification_localdescriptors,
author = {Person Re-identification and Bingpeng Ma and Yu Su and Frédéric Jurie},
title = {Local Descriptors encoded by Fisher Vectors for},
year = {}
}
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Abstract
Abstract. This paper proposes a new descriptor for person reidentification building on the recent advances of Fisher Vectors. Specifically, a simple vector of attributes consisting in the pixel coordinates, its intensity as well as the first and second-order derivatives is computed for each pixel of the image. These local descriptors are turned into Fisher Vectors before being pooled to produce a global representation of the image. The so-obtained Local Descriptors encoded by Fisher Vector (LDFV) have been validated through experiments on two person re-identification benchmarks (VIPeR and ETHZ), achieving state-of-the-art performance on both datasets. 1 Introduction and related works In recent years, person re-identification in unconstrained conditions have attracted more and more research interest. Person re-identification consists in recognizing an individual through different images (e.g. coming from cameras in a distributed network or from the same camera at different time). The key issue
Keyphrases
fisher vector local descriptor person reidentification building key issue recent year person re-identification different image state-of-the-art performance so-obtained local descriptor different time second-order derivative global representation recent advance person re-identification benchmark person re-identification consists research interest new descriptor simple vector distributed network unconstrained condition
