Title :
A feature-based approach to people re-identification using skeleton keypoints
Author :
Munaro, Matteo ; Ghidoni, Stefano ; Dizmen, Deniz Tartaro ; Menegatti, Emanuele
Author_Institution :
Intell. Autonomous Syst. Lab. (IAS-Lab.), Univ. of Padua, Padua, Italy
fDate :
May 31 2014-June 7 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.
Keywords :
image colour analysis; image recognition; image resolution; mobile robots; video surveillance; 3D feature descriptor; RGB-D sensor; compact feature-based signature; feature-based approach; mobile robotics; people re-identification; public datasets; public video surveillance dataset; recognition accuracy; resolution images; skeletal information; skeleton joints; skeleton keypoints; state-of-the-art 2D feature descriptor; visible joint; Joints; Robots; Target tracking; Testing; Three-dimensional displays; Training;
Conference_Titel :
Robotics and Automation (ICRA), 2014 IEEE International Conference on
Conference_Location :
Hong Kong
DOI :
10.1109/ICRA.2014.6907689