Eye Semantic Segmentation with a Lightweight Model

2019-11-04 · Van Thong Huynh, Soo-Hyung Kim, Guee-Sang Lee, Hyung-Jeong Yang

In this paper, we present a multi-class eye segmentation method that can run the hardware limitations for real-time inference. Our approach includes three major stages: get a grayscale image from the input, segment three distinct eye region with a deep network, and remove incorrect areas with heuristic filters. Our model based on the encoder decoder structure with the key is the depthwise convolution operation to reduce the computation cost. We experiment on OpenEDS, a large scale dataset of eye images captured by a head-mounted display with two synchronized eye facing cameras. We achieved the mean intersection over union (mIoU) of 94.85% with a model of size 0.4 megabytes. The source code are available https://github.com/th2l/Eye_VR_Segmentation

📄 PDF Abstract BibTeX arXiv:1911.01049

Code (1)

th2l/Eye_VR_Segmentation 공식 구현 pytorch

Tasks

DecodermodelSemantic Segmentation

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