Region-based parametric motion segmentation using color information
Graphical Models and Image Processing
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Segmentation by MAP Labeling of Watershed Segments
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Markovian framework for foreground-background-shadow separation of real world video scenes
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Simultaneous motion estimation and segmentation
IEEE Transactions on Image Processing
Spatiotemporal video segmentation based on graphical models
IEEE Transactions on Image Processing
Efficient moving object segmentation algorithm using background registration technique
IEEE Transactions on Circuits and Systems for Video Technology
Automatic segmentation of moving objects in video sequences: a region labeling approach
IEEE Transactions on Circuits and Systems for Video Technology
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This paper presents a probabilistic approach for automatically segmenting foreground objects from a video sequence. In order to save computation time and be robust to noise effect, a region detection algorithm incorporating edge information is first proposed to identify the regions of interest. Next, we consider the motion of the foreground objects, and hence utilize the temporal coherence property on the regions detected. Thus, foreground segmentation problem is formulated as follows. Given two consecutive image frames and the segmentation result obtained priorly, we simultaneously estimate the motion vector field and the foreground segmentation mask in a mutually supporting manner. To represent the conditional joint probability density function in a compact form, a Bayesian network is adopted, which is derived to model the interdependency of these two elements. Experimental results for several video sequences are provided to demonstrate the effectiveness of our proposed approach.