TY - GEN
T1 - Video Event Recognition by Dempster-Shafer Theory
AU - Hong, Xin
AU - Huang, Yan
AU - Ma, WenJun
AU - Miller, Paul
AU - Liu, Weiru
AU - Zhou, Huiyu
PY - 2014/8/1
Y1 - 2014/8/1
N2 - 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.
AB - 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.
U2 - 10.3233/978-1-61499-419-0-1031
DO - 10.3233/978-1-61499-419-0-1031
M3 - Conference Contribution (Conference Proceeding)
SN - 9781614994183
T3 - Frontiers in Artificial Intelligence and Applications
SP - 1031
EP - 1032
BT - 21 European Conference on Artificial Intelligence (ECAI 2014)
PB - IOS Press
ER -