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On Recognizing Actions in Still Images via Multiple Features
Fadime Sener1, Cagdas Bas2, and Nazli Ikizler-Cinbis2
1Computer Engineering Department, Bilkent University, Ankara, Turkey
2Computer Engineering Department, Hacettepe University, Ankara, Turkey
Abstract. We propose a multi-cue based approach for recognizing human actions in still images, where relevant object regions are discovered and utilized in a weakly supervised manner. Our approach does not require any explicitly trained object detector or part/attribute annotation. Instead, a multiple instance learning approach is used over sets of object hypotheses in order to represent objects relevant to the actions. We test our method on the extensive Stanford 40 Actions dataset [1] and achieve significant performance gain compared to the state-of-the-art. Our results show that using multiple object hypotheses within multiple instance learning is effective for human action recognition in still images and such an object representation is suitable for using in conjunction with other visual features.
LNCS 7585, p. 263 ff.
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© Springer-Verlag Berlin Heidelberg 2012