A Hybrid Model for Concurrent Interaction Recognition from Videos

M. Sivarathinabala, S. Abirami

Abstract


Human behavior analysis plays an important role in understanding the high-level human activities from surveillance videos. Human behavior has been identified using gestures, postures, actions, interactions and multiple activities of humans. This paper has been analyzed by identifying concurrent interactions, that takes place between multiple peoples. In order to capture the concurrency, a hybrid model has been designed with the combination of Layered Hidden Markov Model (LHMM) and Coupled HMM (CHMM). The model has three layers called as pose layer, action layer and interaction layer, in which pose and action of the single person has been defined in the layered model and the interaction of two persons or multiple persons are defined using CHMM. This hybrid model reduces the training parameters and the temporal correlations over the frames are maintained. The spatial and temporal information are extracted and from the body part attributes, the simple human actions as well as concurrent actions/interactions are predicted. In addition, we further evaluated the results on various datasets also, for analyzing the concurrent interaction between the peoples.


Keywords


Pose prediction, interaction recognition, layered HMM

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References


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DOI: https://doi.org/10.15837/ijccc.2016.4.2524



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