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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Multimodal Background Modeling Using RGB-Depth Features
verfasst von : Rim Trabelsi, Fethi Smach, Issam Jabri, Ammar Bouallegue
Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Verlag: Springer International Publishing
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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.