Metadata only
Datum
2023-06-01Typ
- Journal Article
Abstract
Video segmentation - partitioning video frames into multiple segments or objects - plays a critical role in a broad range of practical applications, from enhancing visual effects in movie, to understanding scenes in autonomous driving, to creating virtual background in video conferencing. Recently, with the renaissance of connectionism in computer vision, there has been an influx of deep learning based approaches for video segmentation that have delivered compelling performance. In this survey, we comprehensively review two basic lines of research - generic object segmentation (of unknown categories) in videos, and video semantic segmentation - by introducing their respective task settings, background concepts, perceived need, development history, and main challenges. We also offer a detailed overview of representative literature on both methods and datasets. We further benchmark the reviewed methods on several well-known datasets. Finally, we point out open issues in this field, and suggest opportunities for further research. We also provide a public website to continuously track developments in this fast advancing field: https://github.com/tfzhou/VS-Survey. Mehr anzeigen
Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
IEEE Transactions on Pattern Analysis and Machine IntelligenceBand
Seiten / Artikelnummer
Verlag
IEEEThema
Object segmentation; Automobiles; Semantic segmentation; Task analysis; Motion segmentation; Deep learning; RoadsOrganisationseinheit
03514 - Van Gool, Luc / Van Gool, Luc