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Recognizing actions from still images

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2008-12-11
İKİZLER CİNBİŞ, NAZLI
Cinbiş, Ramazan Gökberk
PEHLİVAN, SELEN
DUYGULU ŞAHİN, PINAR
In this paper, we approach the problem of understanding human actions from still images. Our method involves representing the pose with a spatial and orientational histogramming of rectangular regions on a parse probability map. We use LDA to obtain a more compact and discriminative feature representation and binary SVMs for classification. Our results over a new dataset collected for this problem show that by using a rectangle histogramming approach, we can discriminate actions to a great extent. We also show how we can use this approach in an unsupervised setting. To our best knowledge, this is one of the first studies that try to recognize actions within still images.