In this paper we propose a novel methodology for people re-identification based on skeletal information. Features are evaluated on the skeleton joints and a highly distinctive and compact feature-based signature is generated for each user by concatenating descriptors of all visible joints. We compared a number of state of the art 2D and 3D feature descriptors to be used with our signature on two newly acquired public datasets for people re-identification with RGB-D sensors. Moreover, we tested our approach against the best re-identification methods in the literature and on a widely used public video surveillance dataset. Our approach proved to be robust to strong illumination changes and occlusions. It achieved very high performance also on low resolution images, overcoming state of the art methods in terms of recognition accuracy and efficiency. These features make our approach particularly suited for mobile robotics.

A Feature-based Approach to People Re-Identification using Skeleton Keypoints

MUNARO, MATTEO;GHIDONI, STEFANO;MENEGATTI, EMANUELE
2014

Abstract

In this paper we propose a novel methodology for people re-identification based on skeletal information. Features are evaluated on the skeleton joints and a highly distinctive and compact feature-based signature is generated for each user by concatenating descriptors of all visible joints. We compared a number of state of the art 2D and 3D feature descriptors to be used with our signature on two newly acquired public datasets for people re-identification with RGB-D sensors. Moreover, we tested our approach against the best re-identification methods in the literature and on a widely used public video surveillance dataset. Our approach proved to be robust to strong illumination changes and occlusions. It achieved very high performance also on low resolution images, overcoming state of the art methods in terms of recognition accuracy and efficiency. These features make our approach particularly suited for mobile robotics.
2014
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2014)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2759552
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