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

Gaurav Sharma, Frederic Jurie, Cordelia Schmid; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 652-659

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.

Related Material


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[bibtex]
@InProceedings{Sharma_2013_CVPR,
author = {Sharma, Gaurav and Jurie, Frederic and Schmid, Cordelia},
title = {Expanded Parts Model for Human Attribute and Action Recognition in Still Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}