Authors: false; HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY ADV INTELL SYST COMP

Journal: Goal 11: Sustainable cities and communities###25130

Conference: cp

Publisher: This paper proposes a fast and robust multi-people tracking algorithm for mobile platforms equipped with a RGB-D sensor. Our approach features an efficient point cloud depth-based clustering, an HOG-like classification to robustly initialize a person tracking and a person classifier with online learning to manage the person ID matching even after a full occlusion. For people detection, we make the assumption that people move on a ground plane. Tests are presented on a challenging real-world indoor environment and results have been evaluated with the CLEAR MOT metrics. Our algorithm proved to correctly track 96% of people with very limited ID switches and few false positives, with an average frame rate of 25 fps. Moreover, its applicability to robot-people following tasks have been tested and discussed. © 2013 Springer-Verlag.

Published: 265

DOI: 60000481||60000481||60000481||60000481||60000481

Volume: Basso||Munaro||Michieletto||Pagello||Menegatti, Issue: DWV-1053-2022||GDR-2343-2022||GBX-6392-2022||DVN-2014-2022||GEW-8545-2022, Pages: Filippo||Matteo||Stefano||Enrico||Emanuele-Dept Informat Engn||Dept Informat Engn||Dept Informat Engn||Dept Informat Engn||Dept Informat Engn

Keywords: 9783642339257