Computing occluding and transparent motions
International Journal of Computer Vision
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Tracking with Bayesian Estimation of Dynamic Layer Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Smoothness in Layers: Motion segmentation using nonparametric mixture estimation.
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
A multi-body factorization method for motion analysis
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Accurate Motion Layer Segmentation and Matting
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Learning Layered Motion Segmentations of Video
International Journal of Computer Vision
Dense point trajectories by GPU-accelerated large displacement optical flow
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Object segmentation by long term analysis of point trajectories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Recently several methods for background subtraction from moving camera were proposed. They use bottom up cues to segment video frames into foreground and background regions. Due to this lack of explicit models, they can easily fail to detect a foreground object when such cues are ambiguous in certain parts of the video. This becomes even more challenging when videos need to be processed online. We present a method which enables learning of pixel based models for foreground and background regions and, in addition, segments each frame in an online framework. The method uses long term trajectories along with a Bayesian filtering framework to estimate motion and appearance models. We compare our method to previous approaches and show results on challenging video sequences.