Unsupervised activity analysis and monitoring algorithms for effective surveillance systems
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
An adaptation framework for head-pose classification in dynamic multi-view scenarios
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Head direction estimation from low resolution images with scene adaptation
Computer Vision and Image Understanding
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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 body classifiers, accounting for the restriction in the configurations that the head and body pose can jointly take. Third, we propose a kernel-formulation of this principle that can be efficiently solved using a global optimization scheme. The method is applied to body and head features computed from automatically extracted body and head location tracks. Thorough experiments on several datasets demonstrate the validity of our approach, the benefit of the coupled adaptation, and that the method performs similarly or better than a state-of-the-art algorithm.