Online Detection of Action Start via Soft Computing for Smart City

Soft computing is facing a rapid evolution thanks to the development of artificial intelligence especially the deep learning. With video surveillance technologies of soft computing, such as image processing, computer vision, and pattern recognition combined with cloud computing, the construction of...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on industrial informatics Jg. 17; H. 1; S. 524 - 533
Hauptverfasser: Wang, Tian, Chen, Yang, Lv, Hongqiang, Teng, Jing, Snoussi, Hichem, Tao, Fei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Schlagworte:
Action start
Artificial Intelligence
Cloud computing
Computer Science
Computer vision
Computer Vision and Pattern Recognition
Image Processing
Machine learning
Multimedia
Object recognition
online detection
Pattern recognition
Real-time systems
Semantics
Smart cities
smart city
Soft computing
Streaming media
Task analysis
video analysis
soft computing
Action start
online detection
video analysis
cloud computing
smart city
ISSN:1551-3203, 1941-0050
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Soft computing is facing a rapid evolution thanks to the development of artificial intelligence especially the deep learning. With video surveillance technologies of soft computing, such as image processing, computer vision, and pattern recognition combined with cloud computing, the construction of smart cities could be maintained and greatly enhanced. In this article, we focus on the online detection of action start task in video understanding and analysis, which is critical to the multimedia security in smart cities. We propose a novel model to tackle this problem and achieves state-of-the-art results on the benchmark THUMOS14 data set.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.2997032