Grasp Type Revisited: A Modern Perspective on a Classical Feature for Vision

Yezhou Yang, Cornelia Fermuller, Yi Li, Yiannis Aloimonos; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 400-408

Abstract


The grasp type provides crucial information about human action. However, recognizing the grasp type in unconstrained scenes is challenging because of the large variations in appearance, occlusions and geometric distortions. In this paper, first we present a convolutional neural network to classify functional hand grasp types. Experiments on a public static scene hand data set validate good performance of the presented method. Then we present two applications utilizing grasp type classification: (a) inference of human action intention and (b) fine level manipulation action segmentation. Experiments on both tasks demonstrate the usefulness of grasp type as a cognitive feature for computer vision. This study shows that the grasp type is a powerful symbolic representation for action understanding, and thus opens new avenues for future research.

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[bibtex]
@InProceedings{Yang_2015_CVPR,
author = {Yang, Yezhou and Fermuller, Cornelia and Li, Yi and Aloimonos, Yiannis},
title = {Grasp Type Revisited: A Modern Perspective on a Classical Feature for Vision},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}