Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Moving Target Classification and Tracking from Real-time Video
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Face Components Detection Using SURF Descriptors and SVMs
IMVIP '08 Proceedings of the 2008 International Machine Vision and Image Processing Conference
Fast point feature histograms (FPFH) for 3D registration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera
Proceedings of the 24th annual ACM symposium on User interface software and technology
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Markerless motion capture of interacting characters using multi-view image segmentation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Efficient regression of general-activity human poses from depth images
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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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.