Paper Info
Title
Video Event Recognition by Dempster-Shafer Theory.
Abstract
This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method employing evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high-level events with semantic meaning along with degrees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78% and 100% for four composite events are encouraging.
Year
DOI
Venue
2014
10.3233/978-1-61499-419-0-1031
Frontiers in Artificial Intelligence and Applications
Field
DocType
Volume
Computer science,Artificial intelligence,Analytics,Train,Dempster–Shafer theory,Machine learning,Event recognition
Conference
263
ISSN
Citations 
PageRank 
0922-6389
1
0.35
References 
Authors
1
6
Name
Order
Citations
PageRank
Xin Hong172.13
Yan Huang256844.91
Wenjun Ma3819.51
Paul C. Miller420112.04
Weiru Liu51597112.05
Huiyu Zhou61303111.91