TY - JOUR AU - Albert Clapes AU - Miguel Reyes AU - Sergio Escalera PY - 2013// TI - Multi-modal User Identification and Object Recognition Surveillance System T2 - PRL JO - Pattern Recognition Letters SP - 799 EP - 808 VL - 34 IS - 7 PB - Elsevier KW - Multi-modal RGB-Depth data analysis KW - User identification KW - Object recognition KW - Intelligent surveillance KW - Visual features KW - Statistical learning N2 - We propose an automatic surveillance 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 using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. 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. UR - http://dx.doi.org/10.1016/j.patrec.2012.12.008 L1 - http://refbase.cvc.uab.es/files/CRE2013.pdf N1 - HUPBA; 600.046; 605.203;MILAB ID - Albert Clapes2013 ER -