Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Using Histograms to Detect and Track Objects in Color Video
AIPR '01 Proceedings of the 30th on Applied Imagery Pattern Recognition Workshop
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient hierarchical method for background subtraction
Pattern Recognition
Color image segmentation: Rough-set theoretic approach
Pattern Recognition Letters
Background Subtraction Based on Local Orientation Histogram
APCHI '08 Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction
Difference of Gaussian Edge-Texture Based Background Modeling for Dynamic Traffic Conditions
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Background Subtraction Techniques: Systematic Evaluation and Comparative Analysis
ACIVS '09 Proceedings of the 11th International Conference on Advanced Concepts for Intelligent Vision Systems
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Background subtraction for automated multisensor surveillance: a comprehensive review
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Dynamic Background Subtraction Using Spatial-Color Binary Patterns
ICIG '11 Proceedings of the 2011 Sixth International Conference on Image and Graphics
Iterative division and correlograms for detection and tracking of moving objects
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Hybrid center-symmetric local pattern for dynamic background subtraction
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
Image and Vision Computing
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Detection of moving objects in the presence of challenging background situations like swaying vegetation, rippling water, camera jitter etc., is known to be a difficult task. Background subtraction is considered to be better than the other approaches in terms of robustness. Its success primarily depends on the proper choice of background model(s) associated with every pixel for its foreground/background labeling. In this work, we have adopted rough-set theoretic measures to embed the spatial similarity around a neighborhood as a model for the pixel. Basic histon and its associated measure Basic Histon Roughness Index (BHRI) have been reported in the literature. It was applied to still image segmentation with impressive performance. Its adoption in video sequences for foreground/background labeling is proposed herein. We extended the histon concept to a 3D histon, which considers the intensities across the color planes in a combined manner, instead of considering independent color planes. Further, we also incorporated fuzziness into the 3D HRI measure. The labeling decision is based on Bhattacharyya distance between the model HRI and the corresponding measure in the current frame. Adoption of rough set theoretic concept into moving object segmentation is nontrivial, as the model updating requires careful consideration so that the pixels associated with gradually changing background or dynamic background are labeled as background and at the same time, slow moving objects are never adopted into the background model. A novel background model update strategy proposed herein takes these into consideration and also eliminates the need of having exclusive ideal background frame initially.