10.1109/FG.2017.69
https://zenodo.org/records/1135101
oai:zenodo.org:1135101
Wenxuan Mou
Wenxuan Mou
Queen Mary, Univ. of London
Christos Tzelepis
Christos Tzelepis
Queen Mary, Univ. of London
Vasileios Mezaris
Vasileios Mezaris
Centre for Res. & Technol. Hellas, Inf. Technol. Inst.
Generic to Specific Recognition Models for Membership Analysis in Group Videos
Zenodo
2017
2017-06-30
eng
https://zenodo.org/communities/moving-h2020
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
Automatic understanding and analysis of groups has attracted increasing attention in the vision and multimedia communities in recent years. However, little attention has been paid to the automatic analysis of group membership - i.e., recognizing which group the individual in question is part of. This paper presents a novel two-phase Support Vector Machine (SVM) based specific recognition model that is learned using an optimized generic recognition model. We conduct a set of experiments using a database collected to study group analysis from multimodal cues while each group (i.e., four participants together) were watching a number of long movie segments. Our experimental results show that the proposed specific recognition model (52%) outperforms the generic recognition model trained across all different videos (35%) and the independent recognition model trained directly on each specific video (33%) using linear SVM.
European Commission
10.13039/501100000780
693092
Training towards a society of data-savvy information professionals to enable open leadership innovation