Computer Vision and Image Understanding
Background modeling by subspace learning on spatio-temporal patches
Pattern Recognition Letters
Space-time spectral model for object detection in dynamic textured background
Pattern Recognition Letters
State of the Art Report on Video-Based Graphics and Video Visualization
Computer Graphics Forum
Background subtraction using low rank and group sparsity constraints
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Block-Sparse RPCA for consistent foreground detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Background subtraction with dirichlet processes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
A new framework for background subtraction using multiple cues
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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
Human activity modeling by spatio temporal textural appearance
Pattern Recognition Letters
Bagadus: An integrated real-time system for soccer analytics
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special issue of best papers of ACM MMSys 2013 and ACM NOSSDAV 2013
Using adaptive background subtraction into a multi-level model for traffic surveillance
Integrated Computer-Aided Engineering
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Background subtraction is one of the key techniques for automatic video analysis, especially in the domain of video surveillance. Although its importance, evaluations of recent background subtraction methods with respect to the challenges of video surveillance suffer from various shortcomings. To address this issue, we first identify the main challenges of background subtraction in the field of video surveillance. We then compare the performance of nine background subtraction methods with post-processing according to their ability to meet those challenges. Therefore, we introduce a new evaluation data set with accurate ground truth annotations and shadow masks. This enables us to provide precise in-depth evaluation of the strengths and drawbacks of background subtraction methods.