Opportunistic Feature Fusion Based Segmentation for Human Gesture Analysis in Vision Networks
نویسندگان
چکیده
An image segmentation method for human gesture analysis is proposed deriving from the concept of opportunistic vision-based feature fusion. The method for human body part segmentation in image sequences is motivated by a layered and collaborative architecture for gesture analysis in multi-view camera networks. The layered structure aims to accommodate the diversity of gestures while collaboration embodies opportunistic fusion of information from multiple cameras. The proposed segmentation method is based on both motion and color information due to the complementary information represented by the two features. This method uses results from optical flow and background subtraction to initiate markers for a watershed algorithm to find the person’s silhouette. Then K-means clustering is used for body part segmentation within the silhouette. Examples are given to illustrate complementary effects of different features and segmentation results. Potentials in feature fusion between multiple cameras are also discussed.
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