Robust spatio-Temporal features for human interaction recognition via artificial neural network
KAUST DepartmentImage and Characterization Center, King Abduallah Univeristy of Science and Technology, Thuwal, Saudi Arabia
Online Publication Date2019-01-18
Print Publication Date2018-12
Permanent link to this recordhttp://hdl.handle.net/10754/656531
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AbstractHuman Interaction Recognition plays a key role in identification of usual and unusual human behaviors and facilitates public dealings, violence detection, robots perception, and virtual entertainments. This paper presents a novel human interaction recognition (HIR) system to recognize human interactions in continuous image sequences. The proposed technology segments full body silhouettes and identifies key body points to extract robust spatio-Temporal features having distinct characteristics for each interaction. Our descriptors focus on local descriptions, capture intensity variations, point-To-point distances and time based relations. The system is trained through artificial neural network to recognize six basic interactions taken from UT-Interaction dataset.
CitationMahmood, M., Jalal, A., & Sidduqi, M. A. (2018). Robust Spatio-Temporal Features for Human Interaction Recognition Via Artificial Neural Network. 2018 International Conference on Frontiers of Information Technology (FIT). doi:10.1109/fit.2018.00045
SponsorsThis research is supported by the Engineering and Managing information Centers, Saudi Arabia, under the “NVorio 5.5 Software program” (Access No. AFRT-2-04-827502) cooperated with the SNCIS (Saudi National Centre for Innovation Science).
Conference/Event name16th International Conference on Frontiers of Information Technology, FIT 2018