Segmenting human hand is important in computer vision applications, for example, sign language interpretation, human computer\ninteraction, and gesture recognition. However, some serious bottlenecks still exist in hand localization systems such as fast hand\nmotion capture, hand over face, and hand occlusions on which we focus in this paper.We present a novel method for hand tracking\nand segmentation based on augmented graph cuts and dynamic model. First, an effective dynamic model for state estimation is\ngenerated, which correctly predicts the location of hands probably having fast motion or shape deformations. Second, new energy\nterms are brought into the energy function to develop augmented graph cuts based on some cues, namely, spatial information,\nhand motion, and chamfer distance. The proposed method successfully achieves hand segmentation even though the hand passes\nover other skin-colored objects. Some challenging videos are provided in the case of hand over face, hand occlusions, dynamic\nbackground, and fast motion. Experimental results demonstrate that the proposed method is much more accurate than other graph\ncuts-based methods for hand tracking and segmentation.
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