TY - GEN
T1 - Local group relationship analysis for group activity recognition
AU - Lee, Dong Gyu
AU - Kim, Pil Soo
AU - Lee, Seong Whan
N1 - Funding Information:
This work was partly supported by the ICT R&D program of MSIP/IITP[B0101-15-0552, Development of Predictive Visual Intelligence Technology].
Publisher Copyright:
© 2017 Institute of Control, Robotics and Systems - ICROS.
PY - 2017/12/13
Y1 - 2017/12/13
N2 - In this paper, we present an approach that exploits local group relationship to tackle the human group activity recognition problem. Specifically, rather than analyze every human motion, we first grouping individual human object into local groups to represent the relationship in the overall scene. The important movement information is maximized by modeling both each human motion and local group relationships. The gated recurrent unit model has been adopted to handle an arbitrary length of trajectory information with non-linear hidden units. In our experiment on public human group activity dataset, we compared the performance of proposed method with that of other competing methods and showed that the proposed method outperforms others.
AB - In this paper, we present an approach that exploits local group relationship to tackle the human group activity recognition problem. Specifically, rather than analyze every human motion, we first grouping individual human object into local groups to represent the relationship in the overall scene. The important movement information is maximized by modeling both each human motion and local group relationships. The gated recurrent unit model has been adopted to handle an arbitrary length of trajectory information with non-linear hidden units. In our experiment on public human group activity dataset, we compared the performance of proposed method with that of other competing methods and showed that the proposed method outperforms others.
KW - Gated recurrent unit
KW - Group activity recognition
KW - Local group relationship
KW - Video surveillance
UR - http://www.scopus.com/inward/record.url?scp=85044464831&partnerID=8YFLogxK
U2 - 10.23919/ICCAS.2017.8204447
DO - 10.23919/ICCAS.2017.8204447
M3 - Conference contribution
AN - SCOPUS:85044464831
T3 - International Conference on Control, Automation and Systems
SP - 236
EP - 238
BT - ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings
PB - IEEE Computer Society
T2 - 17th International Conference on Control, Automation and Systems, ICCAS 2017
Y2 - 18 October 2017 through 21 October 2017
ER -