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We are not contortionists: Coupled adaptive learning for head and body orientation estimation in surveillance video
2012 IEEE Conference on Computer Vision and Pattern Recognition
In this paper, we deal with the estimation of body and head poses (i.e orientations) in surveillance videos, and we make three main contributions. First, we address this issue as a joint model adaptation problem in a semi-supervised framework. Second, we propose to leverage the adaptation on multiple information sources (external labeled datasets, weak labels provided by the motion direction, data structure manifold), and in particular, on the coupling at the output level of the head and bodydoi:10.1109/cvpr.2012.6247845 dblp:conf/cvpr/ChenO12 fatcat:2grec7xgirb3lnl4prit3sjpou