Vision-based Recognition of Human Behaviour for Intelligent Environments

Abstract

A critical requirement for achieving ubiquity of artificial intelligence is to provide intelligent environments with the ability to recognize and understand human behaviour. If this is achieved, proactive interaction can occur and, more interestingly, a great variety of services can be developed. In this thesis we aim to support the development of ambient-assisted living services with advances in human behaviour analysis. Specifically, visual data analysis is considered in order to detect and understand human activity at home. As part of an intelligent monitoring system, single- and multi-view recognition of human actions is performed, along several optimizations and extensions. The present work may pave the way for more advanced human behaviour analysis techniques, such as the recognition of activities of daily living, personal routines and abnormal behaviour detection.