This paper tackles the problem of people re-identification by using soft biometrics features. The method works on RGB-D data (color point clouds) to determine the best matching among a database of possible users. For each subject under testing, skeletal information in three-dimensions is used to regularize the pose and to create a skeleton standard posture (SSP). A partition grid, whose sizes depend on the SSP, groups the samples of the point cloud accordingly to their position. Every group is then studied to build the person signature. The same grid is then used for the other subjects of the database to preserve information about possible shape differences among users. The effectiveness of this novel method has been tested on three public datasets. Numerical experiments demonstrate an improvement of results with reference to the current state-of-the-art, with recognition rates of 97.84% (on a partition of BIWI RGBD-ID), 61.97% (KinectREID) and 89.71% (RGBD-ID), respectively.

People re-identification using skeleton standard posture and color descriptors from RGB-D data

Patruno C;Marani R;Cicirelli G;Stella E;D'Orazio T
2019

Abstract

This paper tackles the problem of people re-identification by using soft biometrics features. The method works on RGB-D data (color point clouds) to determine the best matching among a database of possible users. For each subject under testing, skeletal information in three-dimensions is used to regularize the pose and to create a skeleton standard posture (SSP). A partition grid, whose sizes depend on the SSP, groups the samples of the point cloud accordingly to their position. Every group is then studied to build the person signature. The same grid is then used for the other subjects of the database to preserve information about possible shape differences among users. The effectiveness of this novel method has been tested on three public datasets. Numerical experiments demonstrate an improvement of results with reference to the current state-of-the-art, with recognition rates of 97.84% (on a partition of BIWI RGBD-ID), 61.97% (KinectREID) and 89.71% (RGBD-ID), respectively.
2019
Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato - STIIMA (ex ITIA)
People re-identification; Color-based descriptor; Skeleton standard posture; Partition grid; RGB-D sensor; Color point cloud
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Descrizione: People Re-identification using Skeleton Standard Posture and Color Descriptors from RGB-D Data
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/350360
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