Hand Gesture Segmentation against Complex Background based on Improved Atrous Spatial Pyramid Pooling

Zhenchao Cui, Yu Lei, Yuxiao Wang, Wenzhu Yang, Jing Qi

Abstract: Gesture segmentation is an essential part of gesture detection. The accuracy of gesture detection can be improved by using gesture segmentation to remove the background part un-hand images. However, the inaccurate features of current methods can greatly affect the accuracy of results in segmentation and gesture recognition. In order to solve this problem and obtain accurate features, this paper proposes the improved atrous spatial pyramid pooling (IASPP). IASPP is a pooling layer in convolution neural network, which can refine features by connecting cascade model and parallel model in atrous spatial pyramid pooling. Otherwise, in order to improve the segmentation performance by integrating details and spatial location information at different levels, the IASPP is embedded in the encoder-decoder, and we name the method the improved atrous spatial pyramid pooling-ResNet (IASPP-ResNet) for gesture segmentation. In the experiment part of this paper, we test the proposed method by comparing it with the states of art on the two datasets of OUTHANDS and HGR. It can be seen that IASPP-ResNet can achieve 97.75% Pixel Accuracy and 89.60% MIoU on the OUTHANDS dataset. The Pixel Accuracy and MIoU of the presented method on the HGR dataset can reach 99.09% and 97.52%, respectively. These presented that our method is superior to the states of art.