Random forest based gesture segmentation from depth image

Abstract

Gesture image segmentation is a challenge task due to the high degree of freedom of human gestures, large differences in shape and high flexibility, traditional pattern recognition and image processing methods are not effective in gesture detection. The traditional image segmentation based on the detection of skin color and the image of the depth image are limited by the effects of ambient light, skin color difference and image depth variation, resulting in unsatisfactory results. Therefore, we propose a hand gesture depth image segmentation method based on random forest. The method learns the gesture image feature representation of the depth image by supervising learning. Experiments show that the proposed method segments the gesture s’ pixels from the backgrounds area of the depth image. The proposed method potential has widely usages in gesture tracking, gesture recognition and human computer interaction.