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
Space-time Zernike Moments and Pyramid Kernel Descriptors for Action Classification / Costantini, L.; Seidenari, L.; Serra, G.; Del Bimbo, A.; Capodiferro, L.. - STAMPA. - (2011), pp. 199-208. (Intervento presentato al convegno International Conference on Image Analysis and Processing nel 2011-September).
Space-time Zernike Moments and Pyramid Kernel Descriptors for Action Classification
Seidenari, L.;Serra, G.;Del Bimbo, A.;
2011
Abstract
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 descriptorsFile | Dimensione | Formato | |
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