Expanded Parts Model for Human Attribute and Action Recognition in Still Images

Gaurav Sharma 1, 2 Frédéric Jurie 1, 3 Cordelia Schmid 1
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
3 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : We propose a new model for recognizing human attributes (e.g. wearing a suit, sitting, short hair) and actions (e.g. running, riding a horse) in still images. The proposed model relies on a collection of part templates which are learnt discriminatively to explain specific scale-space locations in the images (in human centric coordinates). It avoids the limitations of highly structured models, which consist of a few (i.e. a mixture of) 'average' templates. To learn our model, we propose an algorithm which automatically mines out parts and learns corresponding discriminative templates with their respective locations from a large number of candidate parts. We validate the method on recent challenging datasets: (i) Willow 7 actions [7], (ii) 27 Human Attributes (HAT) [25], and (iii) Stanford 40 actions [37]. We obtain convincing qualitative and state-of-the-art quantitative results on the three datasets.
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Conference papers
CVPR 2013 - International Conference on Computer Vision and Pattern Recognition, Jun 2013, Portland, Oregon, United States. IEEE, pp.652-659, 2013, <10.1109/CVPR.2013.90>
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Gaurav Sharma, Frédéric Jurie, Cordelia Schmid. Expanded Parts Model for Human Attribute and Action Recognition in Still Images. CVPR 2013 - International Conference on Computer Vision and Pattern Recognition, Jun 2013, Portland, Oregon, United States. IEEE, pp.652-659, 2013, <10.1109/CVPR.2013.90>. <hal-00816144v2>

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