Multimodal background modeling using RGB-depth features

Abstract

This paper presents a method of background subtraction that uses multimodal information, specifically depth and appearance cues, to robustly separate the foreground in dynamic indoor scenes. To this end, RGB-Depth data from a Microsoft Kinect sensor are exploited. We propose an extension of one from the most effective technique for background modeling in real time: Kernel Density Estimation with Fast Gauss Transform technique. Experimental results show that our proposed deals well with gradual/sudden illumination changes, shadows and dynamic backgrounds.