Recognition of actions in daily living is challenging because: (1) actions are continuous; (2) human location is changeful; (3) human body is partially occluded sometimes.This paper proposes a multi-view framework for on-line recognition of actions in daily living.Action representation based on "Envelope shape" enables view-invariant action recognition.A sliding window concatenates the feature vectors for action representation into a stream as the input to a bank of HMMs.A maximum likelihood based classifier detects action.The HMMs are chosen by an ontology based environment knowledge model.Besides

occlusion detection and action representation in occlusion are also discussed.Implementations demonstrate the efficacy of our approach.