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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Space-Time Zernike Moments and Pyramid Kernel Descriptors for Action Classification
verfasst von : Luca Costantini, Lorenzo Seidenari, Giuseppe Serra, Licia Capodiferro, Alberto Del Bimbo
Erschienen in: Image Analysis and Processing – ICIAP 2011
Verlag: Springer Berlin Heidelberg
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Action recognition in videos is a relevant and challenging task of automatic semantic video analysis. Most successful approaches exploit local space-time descriptors. These descriptors are usually carefully engineered in order to obtain feature invariance to photometric and geometric variations. The main drawback of space-time descriptors is high dimensionality and efficiency. In this paper we propose a novel descriptor based on 3D Zernike moments computed for space-time patches. Moments are by construction not redundant and therefore optimal for compactness. Given the hierarchical structure of our descriptor we propose a novel similarity procedure that exploits this structure comparing features as pyramids. The approach is tested on a public dataset and compared with state-of-the art descriptors.