Robust Multi-View Change Detection
暂无分享,去创建一个
Pascal Fua | Luigi di Stefano | François Fleuret | Alessandro Lanza | Jérôme Berclaz | P. Fua | F. Fleuret | L. D. Stefano | J. Berclaz | A. Lanza
[1] Aaron F. Bobick,et al. Fast Lighting Independent Background Subtraction , 2004, International Journal of Computer Vision.
[2] Visvanathan Ramesh,et al. Sudden illumination change detection using order consistency , 2004, Image Vis. Comput..
[3] Luigi di Stefano,et al. Detecting Changes in Grey Level Sequences by ML Isotonic Regression , 2006, 2006 IEEE International Conference on Video and Signal Based Surveillance.
[4] I. Haritaoglu,et al. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002 .
[5] Mubarak Shah,et al. A Multiview Approach to Tracking People in Crowded Scenes Using a Planar Homography Constraint , 2006, ECCV.
[6] Luigi di Stefano,et al. Coarse-to-fine strategy for robust and efficient change detectors , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..
[7] Larry S. Davis,et al. Fast illumination-invariant background subtraction using two views: error analysis, sensor placement and applications , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[8] W. Eric L. Grimson,et al. Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).
[9] Naoya Ohta,et al. A statistical approach to background subtraction for surveillance systems , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[10] Bernhard P. Wrobel,et al. Multiple View Geometry in Computer Vision , 2001 .
[11] Pascal Fua,et al. Robust People Tracking with Global Trajectory Optimization , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[12] L. Davis,et al. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002, Proc. IEEE.