摘要
Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various applications.With the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature learning.This paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three years.Specifically,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation perspectives.For FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are summarized.In terms of training,we discuss ideas for enhancing model transferability.In terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed methods.We also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of technology.Finally,based on the above comparisons and discussions,we present research prospects and future directions.
基金
National Natural Science Foundation of China(Nos.61702323 and 62172268)
the Shanghai Municipal Natural Science Foundation,China(No.20ZR1423100)
the Open Fund of Science and Technology on Thermal Energy and Power Laboratory(No.TPL2020C02)
Wuhan 2nd Ship Design and Research Institute,Wuhan,China,the National Key Research and Development Program of China(No.2018YFB1306303)
the Major Basic Research Projects of Natural Science Foundation of Shandong Province,China(No.ZR2019ZD07).
