Video Event Recognition by Dempster-Shafer Theory
Xin Hong, Huang Yan, Wenjun Ma, Miller Paul, Weiru Liu, Zhou Huiyu · Frontiers in artificial intelligence and applications · 2014
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.