基于检测的自适应手势分割方法

基于肤色模型的手势分割是目前常用的一类手势分割方法，而此类方法容易受到类手势肤色背景的影响而导致严重的误分割，并且由于模型参数固定而对不同的手势肤色不具有适应性。针对以上问题，提出了一种先检测手势再自适应分割手势的方法。首先设计了一种基于空洞卷积的主干网络和一套Anchor方案将SSD改进为手势检测模型，通过该模型初步分割出手势ROI以避免类手势肤色背景的影响。然后根据手势ROI建立YCrCb高斯肤色模型，以使肤色模型对不同的手势肤色具有很好的适应能力。实验结果表明，在多种复杂场景下，本文的手势分割算法能够避免类肤色背景的影响并且对不同肤色的手势都取得了非常好的分割效果。 

Adaptive Gesture Segmentation Method Based on Detection

Hand segmentation based on skin color model is a commonly used hand segmentation method. However, this kind of method is easy to be affected by background to cause serious mis-segmentation and is not adaptable to different skin tones. So, this paper proposes a method to roughly detect hand gesture first and then segment hand adaptively. SSD is improved into a hand gesture detection model, where a backbone network based on dilated convolution and a set of Anchor setting scheme are designed, which can preliminarily segment the gesture ROI to avoid the influence of skin color background on the gesture segmentation. Then, the YCrCb gaussian skin color model was built according to the gesture ROI, so that the skin color model could adapt well to different skin colors of gestures. The experimental results demonstrate the robustness and effectiveness of proposed method for gestures with different skin color in a variety of complex scenarios