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.
|Title of host publication||21 European Conference on Artificial Intelligence (ECAI 2014)|
|Number of pages||2|
|Publication status||Published - 1 Aug 2014|
|Name||Frontiers in Artificial Intelligence and Applications|