Abstract
We propose an automatic system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized online using robust statistical approaches over RGBD descriptions. Finally, the system saves the historic of user-object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches.
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Clapés, A., Reyes, M., Escalera, S. (2012). User Identification and Object Recognition in Clutter Scenes Based on RGB-Depth Analysis. In: Perales, F.J., Fisher, R.B., Moeslund, T.B. (eds) Articulated Motion and Deformable Objects. AMDO 2012. Lecture Notes in Computer Science, vol 7378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31567-1_1
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DOI: https://doi.org/10.1007/978-3-642-31567-1_1
Publisher Name: Springer, Berlin, Heidelberg
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