Integrating local action elements for action analysis

  • Authors:

  • Tuan Hue Thi;Li Cheng;Jian Zhang;Li Wang;Shinichi Satoh

  • Affiliations:

  • National ICT of Australia, Australia and School of Computer Science, University of New South Wales, Australia;Bioinformatics Institute, A*STAR, Singapore;National ICT of Australia, Australia and School of Computer Science, University of New South Wales, Australia;Information and Science Technology Institute, Nanjing Forest University, China;Multimedia Information Research Division, National Institute of Informatics, Japan

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2012

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Abstract

In this paper, we propose a framework for human action analysis from video footage. A video action sequence in our perspective is a dynamic structure of sparse local spatial-temporal patches termed action elements, so the problems of action analysis in video are carried out here based on the set of local characteristics as well as global shape of a prescribed action. We first detect a set of action elements that are the most compact entities of an action, then we extend the idea of Implicit Shape Model to space time, in order to properly integrate the spatial and temporal properties of these action elements. In particular, we consider two different recipes to construct action elements: one is to use a Sparse Bayesian Feature Classifier to choose action elements from all detected Spatial Temporal Interest Points, and is termed discriminative action elements. The other one detects affine invariant local features from the holistic Motion History Images, and picks up action elements according to their compactness scores, and is called generative action elements. Action elements detected from either way are then used to construct a voting space based on their local feature representations as well as their global configuration constraints. Our approach is evaluated in the two main contexts of current human action analysis challenges, action retrieval and action classification. Comprehensive experimental results show that our proposed framework marginally outperforms all existing state-of-the-arts techniques on a range of different datasets.