Recognizing actions from still images

Date

2008-12-11

Author

İKİZLER CİNBİŞ, NAZLI
Cinbiş, Ramazan Gökberk
PEHLİVAN, SELEN
DUYGULU ŞAHİN, PINAR

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Image recognition, Image edge detection, Shape measurement, Linear discriminant analysis, Testing, Application software, Surveillance, Human computer interaction, Biological system modeling, Histograms

URI

https://hdl.handle.net/11511/34317

Collections

Department of Computer Engineering, Conference / Seminar