Space-time Zernike moments and pyramid kernel descriptors for action classification

  • Authors:

  • Luca Costantini;Lorenzo Seidenari;Giuseppe Serra;Licia Capodiferro;Alberto Del Bimbo

  • Affiliations:

  • Fondazione Ugo Bordoni, Roma, Italy;Media Integration and Communication Center, University of Florence, Italy;Media Integration and Communication Center, University of Florence, Italy;Fondazione Ugo Bordoni, Roma, Italy;Media Integration and Communication Center, University of Florence, Italy

  • Venue:
  • ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
  • Year:
  • 2011

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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 descriptors.