Detected motion classification with a double-background and a neighborhood-based difference
Pattern Recognition Letters
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recursive Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and Removing Cast Shadows through a Multidistribution Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Study on color space selection for detecting cast shadows in video surveillance: Articles
International Journal of Imaging Systems and Technology - Special Issue on Applied Color Image Processing
Color Space Selection for Moving Shadow Elimination
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Non-parametric background and shadow modeling for object detection
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
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Foreground detection is the most important preprocess for video surveillance applications. However, classifying pixels of video frames into only background and foreground seems insufficient in real situations. In this study, we model the monitoring scene by using a multi-layer framework. The proposed scene model classifies pixels layer by layer into four different states, comprising background, moving foreground, static foreground and shadow. Different scenarios such as shadow elimination, abandoned object detection and object tracking were tested with the proposed scene model. The experimental results demonstrate it is quantified for real video surveillance applications.