title: Automatic Metadata Generation Through Analysis of Narration Within Instructional Videos.
abstract: Current activity recognition based assistive living solutions have adopted relatively rigid models of inhabitant activities. These solutions have some deficiencies associated with the use of these models. To address this, a goal-oriented solution has been proposed. In a goal-oriented solution, goal models offer a method of flexibly modelling inhabitant activity. The flexibility of these goal models can dynamically produce a large number of varying action plans that may be used to guide inhabitants. In order to provide illustrative, video-based, instruction for these numerous actions plans, a number of video clips would need to be associated with each variation. To address this, rich metadata may be used to automatically match appropriate video clips from a video repository to each specific, dynamically generated, activity plan. This study introduces a mechanism of automatically generating suitable rich metadata representing actions depicted within video clips to facilitate such video matching. This performance of this mechanism was evaluated using eighteen video files; during this evaluation metadata was automatically generated with a high level of accuracy.
authors.affiliation: School of Computing and Mathematics, University of Ulster, Newtownabbey, UK, rafferty-j@email.ulster.ac.uk.
authors.forename: Joseph
authors.initials: J
authors.lastname: Rafferty
authors.forename: Chris
authors.initials: C
authors.lastname: Nugent
authors.forename: Jun
authors.initials: J
authors.lastname: Liu
authors.forename: Liming
authors.initials: L
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D006801:Humans; D057187:Independent Living; D018670:Monitoring, Ambulatory / Q000379:methods*; D033262:Narration; D010347:Patient Care Planning; D010353:Patient Education as Topic / Q000379:methods*; D049250:Speech Recognition Software; D014741:Video Recording / Q000379:methods*