In recent years, much effort has been put into the development of novel algorithms to solve the person re-identification problem. The goal is to match a given person's image against a gallery of people. In this paper, we propose a single-shot supervised method to compute a scoring function that, when applied to a pair of images, provides a score expressing the likelihood that they depict the same individual. The method is characterized by: (i) the usage of a set of local image descriptors based on Fisher vectors, (ii) the training of a pool of scoring functions based on the local descriptors, and (iii) the construction of a strong scoring function by means of an adaptive boosting procedure. The method has been tested on four data-sets and results have been compared with state-of-the-art methods clearly showing superior performance.

Boosting Fisher vector based scoring functions for person re-identification

Messelodi, Stefano;Modena, Carla Maria
2015

Abstract

In recent years, much effort has been put into the development of novel algorithms to solve the person re-identification problem. The goal is to match a given person's image against a gallery of people. In this paper, we propose a single-shot supervised method to compute a scoring function that, when applied to a pair of images, provides a score expressing the likelihood that they depict the same individual. The method is characterized by: (i) the usage of a set of local image descriptors based on Fisher vectors, (ii) the training of a pool of scoring functions based on the local descriptors, and (iii) the construction of a strong scoring function by means of an adaptive boosting procedure. The method has been tested on four data-sets and results have been compared with state-of-the-art methods clearly showing superior performance.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/300996
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