基于深度学习的视频语义分割综述

Survey on Video Semantic Segmentation Based on Deep Learning

目前对视频语义分割的研究主要分为两方面,一是如何利用视频帧之间的时序信息提高图像分割的精度;二是如何利用视频帧之间的相似性确定关键帧,减少计算量,提升模型的运行速度.在提升分割精度方面一般设计新的模块,将新模块与现有的CNNs结合;在减少计算量方面,利用帧序列的低层特征相关性选择关键帧,同时减少操作时间.本文首先介绍视频语义分割的发展背景与操作数据集Cityscapes、CamVid;其次,介绍现有的视频语义分割方法;最后总结当前视频语义分割的发展情况,并对未来的发展给出一些展望和建议.

At present,the research on video semantic segmentation is mainly divided into two aspects.The first one is how to improve the accuracy of image segmentation by using timing information between video frames,while the second one is how to use the similarity between the frames to determine the key frame,reduce the amount of calculation,and improve the running speed of the model.In terms of improving segmentation accuracy,new modules are generally designed and combined with existing CNNs.In terms of reducing computation load,the low-level feature correlation of frame sequence is used to select the key frame,which reduces computation load and operation time at the same time.Firstly,this paper introduces the development background and operation datasets Cityscapes and CamVid of video semantic segmentation.Secondly,the existing video semantic segmentation methods are introduced.Finally,it summarizes the current development of video semantic segmentation,and gives some prospects and suggestions for future development.