Shape and motion from image streams under orthography: a factorization method
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
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical background modeling for non-stationary camera
Pattern Recognition Letters
Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Statistical Background Subtraction for a Mobile Observer
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Bayesian Object Detection in Dynamic Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Particle Video: Long-Range Motion Estimation using Point Trajectories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Beyond pixels: exploring new representations and applications for motion analysis
Beyond pixels: exploring new representations and applications for motion analysis
Motion Segmentation in the Presence of Outlying, Incomplete, or Corrupted Trajectories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dense point trajectories by GPU-accelerated large displacement optical flow
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Generalized principal component analysis (GPCA)
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Robust principal component analysis?
Journal of the ACM (JACM)
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Evaluation of background subtraction techniques for video surveillance
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image decomposition via the combination of sparse representations and a variational approach
IEEE Transactions on Image Processing
Unwrapping low-rank textures on generalized cylindrical surfaces
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Background subtraction via coherent trajectory decomposition
Proceedings of the 21st ACM international conference on Multimedia
Nobody likes Mondays: foreground detection and behavioral patterns analysis in complex urban scenes
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
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Background subtraction has been widely investigated in recent years. Most previous work has focused on stationary cameras. Recently, moving cameras have also been studied since videos from mobile devices have increased significantly. In this paper, we propose a unified and robust framework to effectively handle diverse types of videos, e.g., videos from stationary or moving cameras. Our model is inspired by two observations: 1) background motion caused by orthographic cameras lies in a low rank subspace, and 2) pixels belonging to one trajectory tend to group together. Based on these two observations, we introduce a new model using both low rank and group sparsity constraints. It is able to robustly decompose a motion trajectory matrix into foreground and background ones. After obtaining foreground and background trajectories, the information gathered on them is used to build a statistical model to further label frames at the pixel level. Extensive experiments demonstrate very competitive performance on both synthetic data and real videos.