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

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dc.contributor.authorAHOUANDJINOU, SÈMÈVO ARNAUD ROLAND MARTIAL
dc.contributor.authorMOTAMED, Cina
dc.contributor.authorEZIN, C. E.
dc.contributor.authorPINTI, Antonio
dc.date.accessioned2026-06-02T16:06:57Z
dc.date.available2026-06-02T16:06:57Z
dc.date.issued2015
dc.description.abstractHuman 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.
dc.identifier.doi10.1109/IPTA.2015.7367094
dc.identifier.otherBECDB-5613
dc.identifier.urihttps://dspace.uac.bj/handle/123456789/5190
dc.language.isofr
dc.relation.ispartofIEEE International Conference on Image Processing, Theory, Tools and Applications
dc.subjectHuman Activities Recognition
dc.subjectImages sequences
dc.subjectinterpretation
dc.subjectHidden Markov Models
dc.subjectHierarchical and Temporal
dc.subjectreasoning
dc.subjectTransferable Belief Models.
dc.titleCredal Human Activity Recognition Based-HMM by Combining Hierarchical and Temporal Reasoning
dc.typeArticle

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