A temporal belief-based hidden markov model for human action recognition in medical videos

Abstract

In the context of human action recognition from video sequences in the medical environment, a Temporal Belief-based Hidden Markov Model (HMM) is presented. It allows to cope with human action temporality and enables to manage the data uncertainty and the knowledge incompleteness. The system of activity recognition is based on an HMM with explicit state duration. The global interpretation process uses the framework of the Transferable Belief Model (TBM). It enable us to model and manage the uncertainty over the video interpretation process. An application is proposed for human action analysis in medical video sequences provided by a patient monitoring system in the cardiology section in hospital. The proposed recognition method has been assessed on a database of 3000 video images of medical scenes and compared to the performance of the probabilistic Hidden Markov Models.