Pfinder: Real-Time Tracking of the Human Body
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
Online Palmprint Identification
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
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Robust Foreground Detection In Video Using Pixel Layers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis-based background subtraction for indoor surveillance
IEEE Transactions on Image Processing
Spatiotemporal Saliency in Dynamic Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Base selection in estimating sparse foreground in video
ICIP'09 Proceedings of the 16th IEEE international conference on Image 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
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
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
Statistical Background Subtraction Using Spatial Cues
IEEE Transactions on Circuits and Systems for Video Technology
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A novel background subtraction method that can work under complex environments is presented in this paper. The proposed method consists of two stages: coarse foreground detection through the phase based background model we present, and foreground refinement using the distance transform. We first propose a phase feature which is suitable for background modeling. The background model is then built where each pixel is modeled as a group of adaptive phase features. Although the foreground detection result produced by the background model only contains some sparse pixels, the basic structure of the foreground has been captured as a whole. In the next stage, we adopt the distance transform to aggregate the pixels surrounding the foreground so that the final result is more clear and integrated. Our method can handle many complex situations including dynamic background and illumination variations, especially for sudden illumination change. Besides, it has no bootstrapping limitations, which means our method is without background initialization constraints. Experiments on real data sets and comparison with the existing techniques show that the proposed method is effective and robust.