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Author  |
Albert Clapes; Miguel Reyes; Sergio Escalera |
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Title |
User Identification and Object Recognition in Clutter Scenes Based on RGB-Depth Analysis |
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Conference Article |
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Year |
2012 |
Publication |
7th Conference on Articulated Motion and Deformable Objects |
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Volume |
7378 |
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Pages |
1-11 |
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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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Address |
Mallorca |
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Publisher |
Springer Berlin Heidelberg |
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ISSN |
0302-9743 |
ISBN |
978-3-642-31566-4 |
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Conference |
AMDO |
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Notes |
HUPBA;MILAB |
Approved |
no |
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Call Number |
Admin @ si @ CRE2012 |
Serial |
2010 |
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Author |
Albert Clapes; Miguel Reyes; Sergio Escalera |
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Title |
Multi-modal User Identification and Object Recognition Surveillance System |
Type |
Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
34 |
Issue |
7 |
Pages |
799-808 |
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Keywords |
Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning |
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Abstract |
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. |
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Elsevier |
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Notes |
HUPBA; 600.046; 605.203;MILAB |
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no |
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Call Number |
Admin @ si @ CRE2013 |
Serial |
2248 |
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