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. © 2012 Springer-Verlag.
CITATION STYLE
Clapés, A., Reyes, M., & Escalera, S. (2012). User identification and object recognition in clutter scenes based on RGB-depth analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7378 LNCS, pp. 1–11). https://doi.org/10.1007/978-3-642-31567-1_1
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