TY - GEN
T1 - Robust spatio-Temporal features for human interaction recognition via artificial neural network
AU - Mahmood, Maria
AU - Jalal, Ahmad
AU - Sidduqi, M. A.
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This 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).
PY - 2019/1/18
Y1 - 2019/1/18
N2 - Human 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.
AB - Human 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.
UR - http://hdl.handle.net/10754/656531
UR - https://ieeexplore.ieee.org/document/8616994/
UR - http://www.scopus.com/inward/record.url?scp=85062391365&partnerID=8YFLogxK
U2 - 10.1109/FIT.2018.00045
DO - 10.1109/FIT.2018.00045
M3 - Conference contribution
SN - 9781538693551
SP - 218
EP - 223
BT - 2018 International Conference on Frontiers of Information Technology (FIT)
PB - Institute of Electrical and Electronics Engineers Inc.
ER -
