IBM Systems Journal
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
Detecting Salient Motion by Accumulating Directionally-Consistent Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Histograms to Detect and Track Objects in Color Video
AIPR '01 Proceedings of the 30th on Applied Imagery Pattern Recognition Workshop
A linear-time component-labeling algorithm using contour tracing technique
Computer Vision and Image Understanding
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
An adaptive classification system for video-based face recognition
Information Sciences: an International Journal
Efficient visual tracking using particle filter with incremental likelihood calculation
Information Sciences: an International Journal
Silhouette-Based method for object classification and human action recognition in video
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
Data hiding in image and video .I. Fundamental issues and solutions
IEEE Transactions on Image Processing
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
Illumination-insensitive texture discrimination based on illumination compensation and enhancement
Information Sciences: an International Journal
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Background construction is the base of object detection and tracking of machine vision systems. Traditional background modeling methods often require complicated computations and are sensitive to illumination changes. This paper proposes a novel block-based background modeling method based on a hierarchical coarse-to-fine texture description, which fully utilizes the texture characteristics of each incoming frame. The proposed method is efficient and can resist both illumination changes and shadow disturbance. The experimental results show that this method is suitable for real-world scenes and real-time applications.