Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Recursive Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cast shadow segmentation using invariant color features
Computer Vision and Image Understanding
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
A Dynamic Conditional Random Field Model for Foreground and Shadow Segmentation
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 Belief Propagation for Early Vision
International Journal of Computer Vision
Efficient hierarchical method for background subtraction
Pattern Recognition
Learning and Removing Cast Shadows through a Multidistribution Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moving Cast Shadows Detection Using Ratio Edge
IEEE Transactions on Multimedia
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Region-Level Motion-Based Background Modeling and Subtraction Using MRFs
IEEE Transactions on Image Processing
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In this paper, we propose a novel over-segmentation based method for the detection of foreground objects from a surveillance video by integrating techniques of background modeling and Markov Random Fields classification. Firstly, we introduce a fast affinity propagation clustering algorithm to produce the over-segmentation of a reference image by taking into account color difference and spatial relationship between pixels. A background model is learned by using Gaussian Mixture Models with color features of the segments to represent the time-varying background scene. Next, each segment is treated as a node in a Markov Random Field and assigned a state of foreground, shadow and background, which is determined by using hierarchical belief propagation. The relationship between neighboring regions is also considered to ensure spatial coherence of segments. Finally, we demonstrate experimental results on several image sequences to show the effectiveness and robustness of the proposed method.