Credal Human Activity Recognition Based-HMM by Combining Hierarchical and Temporal Reasoning

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AuteurAHOUANDJINOU, SEMEVO ARNAUD ROLAND MARTIAL
AuteurMOTAMED, CINA
AuteurEZIN, C EUGENE
AuteurPINTI, ANTONIO
Date d'ajout2026-06-02T16:06:57Z
Date de disponibilite2026-06-02T16:06:57Z
Date de publication2015
ResumeHuman activities recognition in videos sequences is a very current research topic being investigated in computer vision. This paper offers an approach for video analysis by exploiting hidden Markov models. We propose an extension of the standard model by integrating three abstraction layers through the management of hierarchical structure and the temporal evolution of events. In addition, data imperfections are also managed through a more generic framework than the probabilistic that is the Transferable Belief Model. The proposed approach has been assessed with the “baggage abandoned” scenario of PETS’06 dataset of computer vision community. Lastly, the proposed scenario recognition system performance is analysed and compared to the result of classic HMM models.
DOI10.1109/IPTA.2015.7367094
Autre identifiantBECDB-5613
URIhttps://dspace.uac.bj/handle/123456789/5190
Languefr
Fait partie deIEEE International Conference on Image Processing, Theory, Tools and Applications
SujetHuman Activities Recognition
SujetImages sequences
Sujetinterprétation
SujetHidden Markov Models
SujetHierarchical and Temporal
Sujetreasoning
SujetTransferable Belief Models
TitreCredal Human Activity Recognition Based-HMM by Combining Hierarchical and Temporal Reasoning
TypeArticle

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