Statistical background modelling for tracking with a virtual camera
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Non-parametric Local Transforms for Computing Visual Correspondence
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Real-time stereo vision on the PARTS reconfigurable computer
FCCM '97 Proceedings of the 5th IEEE Symposium on FPGA-Based Custom Computing Machines
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Multi-level background initialization using Hidden Markov Models
IWVS '03 First ACM SIGMM international workshop on Video surveillance
Multi-resolution background modeling of dynamic scenes using weighted match filters
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Background Subtraction Using Markov Thresholds
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Incorporating Object Tracking Feedback into Background Maintenance Framework
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive parametric statistical background subtraction for video segmentation
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Background-Subtraction in Thermal Imagery Using Contour Saliency
International Journal of Computer Vision
Background-subtraction using contour-based fusion of thermal and visible imagery
Computer Vision and Image Understanding
Efficient hierarchical method for background subtraction
Pattern Recognition
Robust background subtraction with foreground validation for urban traffic video
EURASIP Journal on Applied Signal Processing
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Machine Graphics & Vision International Journal
Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology
Scene modeling and change detection in dynamic scenes: A subspace approach
Computer Vision and Image Understanding
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Human silhouette extraction method using region based background subtraction
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Moving object contour detection based on S-T characteristics in surveillance
Proceedings of the 2007 conference on Human interface: Part I
Non-parametric background and shadow modeling for object detection
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Base selection in estimating sparse foreground in video
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Light-weight salient foreground detection for embedded smart cameras
Computer Vision and Image Understanding
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
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Interactive motion analysis for video surveillance and long term scene monitoring
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Robust head detection and tracking in cluttered workshop environments using GMM
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Background updating for visual surveillance
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Adaptive background subtraction with multiple feedbacks for video surveillance
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
A novel robust statistical method for background initialization and visual surveillance
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Gaussian mixture model in improved HLS color space for human silhouette extraction
ICAT'06 Proceedings of the 16th international conference on Advances in Artificial Reality and Tele-Existence
Accurate foreground extraction using graph cut with trimap estimation
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Determination of road traffic parameters based on 3d wavelet representation of an image sequence
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
A new framework for background subtraction using multiple cues
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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Time-Adaptive, Per-Pixel Mixtures Of Gaussians (TAPPMOGs) have recently become a popular choice for robust modeling and removal of complex and changing backgrounds at the pixel level. However, TAPPMOG-based methods cannot easily be made to model dynamic backgrounds with highly complex appearance, or to adapt promptly to sudden "uninteresting" scene changes such as the repositioning of a static object or the turning on of a light, without further undermining their ability to segment foreground objects, such as people, where they occlude the background for too long. To alleviate tradeoffs such as these, and, more broadly, to allow TAPPMOG segmentation results to be tailored to the specific needs of an application, we introduce a general framework for guiding pixel-level TAPPMOG evolution with feedback from "high-level" modules. Each such module can use pixel-wise maps of positive and negative feedback to attempt to impress upon the TAPPMOG some definition of foreground that is best expressed through "higher-level" primitives such as image region properties or semantics of objects and events. By pooling the foreground error corrections of many high-level modules into a shared, pixel-level TAPPMOG model in this way, we improve the quality of the foreground segmentation and the performance of all modules that make use of it. We show an example of using this framework with a TAPPMOG method and high-level modules that all rely on dense depth data from a stereo camera.