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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.

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