System identification: theory for the user
System identification: theory for the user
Lectures & Adaptive Parameter Estimation
Lectures & Adaptive Parameter Estimation
Balanced approximation of stochastic systems
SIAM Journal on Matrix Analysis and Applications
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Elements of information theory
Elements of information theory
The background primal sketch: an approach for tracking moving objects
Machine Vision and Applications
N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video-Based Surveillance Systems: Computer Vision and Distributed Processing
Video-Based Surveillance Systems: Computer Vision and Distributed Processing
International Journal of Computer Vision
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
Incremental Singular Value Decomposition of Uncertain Data with Missing Values
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Background Modeling for Segmentation of Video-Rate Stereo Sequences
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Probabilistic visual learning for object detection
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Analysis of Persistent Motion Patterns Using the 3D Structure Tensor
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Spatio-temporal background models for outdoor surveillance
EURASIP Journal on Applied Signal Processing
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
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A model change detection approach to dynamic scene modeling
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Multimedia Tools and Applications
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Change detection for temporal texture in the Fourier domain
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Rain or Snow Detection in Image Sequences Through Use of a Histogram of Orientation of Streaks
International Journal of Computer Vision
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Computer Vision and Image Understanding
Editor's Choice Article: Human activity recognition in videos using a single example
Image and Vision Computing
Online detection of abnormal events in video streams
Journal of Electrical and Computer Engineering
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Background modeling and subtraction are core components in video processing. To this end, one aims to recover and continuously update a representation of the scene that is compared with the current input to perform subtraction. Most of the existing methods treat each pixel independently and attempt to model the background perturbation through statistical modeling such as a mixture of Gaussians. While such methods have satisfactory performance in many scenarios, they do not model the relationships and correlation amongst nearby pixels. Such correlation between pixels exists both in space and across time especially when the scene consists of image structures moving across space. Waving trees, beach, escalators and natural scenes with rain or snow are examples of such scenes. In this paper, we propose a method for differentiating between image structures and motion that are persistent and repeated from those that are ''new''. Towards capturing the appearance characteristics of such scenes, we propose the use of an appropriate subspace created from image structures. Furthermore, the dynamical characteristics are captured by the use of a prediction mechanism in such subspace. Since the model must adapt to long-term changes in the background, an incremental method for fast online adaptation of the model parameters is proposed. Given such adaptive models, robust and meaningful measures for detection that consider both structural and motion changes are considered. Promising experimental results that include qualitative and quantitative comparisons with existing background modeling/subtraction techniques demonstrate the very promising performance of the proposed framework when dealing with complex backgrounds.