A Video Semantic Segmentation Method Based on FCN and Data Argumentation

Abstract

Video semantic segmentation is an important and fundamental problem in computer vision. It has broad application prospects in the fields of mobile robot, drone, intelligent driving and monitoring. With the development of neural networks, the models commonly adopted are all based on full convolutional network (FCN). However, current methods are limited by a small training set, which makes it difficult to improve the segmentation accuracy. In this paper, we propose a robust method that uses different data argumentation methods to increase the data set according to different characteristics of the scene. On the basis of analyzing different video features, targeted data argumentation techniques are selected to increase training samples. Experimental results show that data argumentation techniques can significantly improve the accuracy of video semantic segmentation compared with traditional training methods that ignore video features.