Video scene parsing: An overview of deep learning methods and datasets

Yan，Xiyu1,2; Gong，Huihui2; Jiang，Yong1,3; Xia，Shu Tao1,3; Zheng，Feng2; You，Xinge4; Shao，Ling5,6

2020-12-01

10.1016/j.cviu.2020.103077

COMPUTER VISION AND IMAGE UNDERSTANDING   影响因子和分区

1077-3142

1090-235X

Video scene parsing (VSP) has become a key problem in the field of computer vision in recent years due to its wide range of applications in numerous domains (e.g., autonomous driving). With the renaissance of deep learning (DL) techniques, various of VSP methods under this framework have demonstrated promising performance. However, no thorough review has been provided to comprehensively summarize the advantages and disadvantages of these methods, their datasets, or the directions for development. To remedy this, we provide an overview of the different DL methods applied to VSP in various scientific and engineering areas. Firstly, we describe several indispensable preliminaries of this field, defining essential background concepts as well as fundamental terminologies and differentiating between VSP and other similar problems. Then, according to their principles, contributions and importance, recent advanced DL methods for VSP are meticulously classified and thoroughly analyzed. Thirdly, we elaborate on the most frequently-used datasets and describe common evaluation metrics for VSP. Besides, extensive of experimental results for the aforementioned methods are presented to demonstrate their advantages and disadvantages. This is followed by further comparisons and discussions on the main challenges faced by researchers. Finally, we sum up the paper by drawing conclusions on the state-of-the-art methods for VSP and highlights potential research orientations as well as promising future work for DL techniques applied to VSP.

Deep Learning overview3 Video Scene Parsing

SCI ; EI

英语

通讯

National Natural Science Foundation of China[61972188][61771273] ; R&D Program of Shenzhen, China[JCYJ20180508152204044][LZC0019]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic

WOS:000591706600001

ACADEMIC PRESS INC ELSEVIER SCIENCE

20203609130019

Industrial research ; Engineering research ; Learning systems

Ergonomics and Human Factors Engineering:461.4 ; Engineering Research:901.3 ; Industrial Engineering:912.1

COMPUTER SCIENCE

2-s2.0-85090024666

Scopus

1.Tsinghua Shenzhen International Graduate School,Tsinghua University (THU),Shenzhen,China
2.Department of Computer Science and Engineering,Southern University of Science and Technology (SUSTech),Shenzhen,518055,China
3.PCL Research Center of Networks and Communications,Peng Cheng Laboratory,Shenzhen,China
4.School of Electronic Information and Communications,Huazhong University of Science and Technology (HUST),Wuhan,430074,China
5.Inception Institute of Artificial Intelligence,Abu Dhabi,United Arab Emirates
6.Mohamed bin Zayed University of Artificial Intelligence,Abu Dhabi,United Arab Emirates

Yan，Xiyu,Gong，Huihui,Jiang，Yong,et al. Video scene parsing: An overview of deep learning methods and datasets[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2020,201.

Yan，Xiyu.,Gong，Huihui.,Jiang，Yong.,Xia，Shu Tao.,Zheng，Feng.,...&Shao，Ling.(2020).Video scene parsing: An overview of deep learning methods and datasets.COMPUTER VISION AND IMAGE UNDERSTANDING,201.

Yan，Xiyu,et al."Video scene parsing: An overview of deep learning methods and datasets".COMPUTER VISION AND IMAGE UNDERSTANDING 201(2020).