REAL-TIME 3D MULTIPLE HUMAN TRACKING WITH ROBUSTNESS ENHANCEMENT THROUGH MACHINE LEARNING

Suraj Nair, Emmanuel Dean-Leon, Alois Knoll

Abstract

This paper presents a novel and robust vision-based real-time 3D multiple human tracking system. It is capable of automatically detecting and tracking multiple humans in real-time even when they occlude each other. Furthermore, it is robust towards drastically changing lighting conditions. The system consists of 2 parts, 1. a vision based human tracking system using multiple visual cues with a robust occlusion handling module, 2. a machine learning based module for intelligent multi-modal fusion and self adapting the system towards drastic light changes. The paper also proposes an approach to validate the system through zero-error ground truth data obtained by virtual environments. The system is also validated in real-world scenarios.

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Paper Citation


in Harvard Style

Nair S., Dean-Leon E. and Knoll A. (2012). REAL-TIME 3D MULTIPLE HUMAN TRACKING WITH ROBUSTNESS ENHANCEMENT THROUGH MACHINE LEARNING . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-04-4, pages 359-366. DOI: 10.5220/0003824203590366


in Bibtex Style

@conference{visapp12,
author={Suraj Nair and Emmanuel Dean-Leon and Alois Knoll},
title={REAL-TIME 3D MULTIPLE HUMAN TRACKING WITH ROBUSTNESS ENHANCEMENT THROUGH MACHINE LEARNING},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={359-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003824203590366},
isbn={978-989-8565-04-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012)
TI - REAL-TIME 3D MULTIPLE HUMAN TRACKING WITH ROBUSTNESS ENHANCEMENT THROUGH MACHINE LEARNING
SN - 978-989-8565-04-4
AU - Nair S.
AU - Dean-Leon E.
AU - Knoll A.
PY - 2012
SP - 359
EP - 366
DO - 10.5220/0003824203590366