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Action Recognition

The Experts below are selected from a list of 20280 Experts worldwide ranked by ideXlab platform

Ioannis Pitas – 1st expert on this subject based on the ideXlab platform

  • Multi-view Human Action Recognition: A Survey
    2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2013
    Co-Authors: Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

    Abstract:

    While single-view human Action Recognition has attracted considerable research study in the last three decades, multi-view Action Recognition is, still, a less exploited field. This paper provides a comprehensive survey of multi-view human Action Recognition approaches. The approaches are reviewed following an application-based categorization: methods are categorized based on their ability to operate using a fixed or an arbitrary number of cameras. Finally, benchmark databases frequently used for evaluation of multi-view approaches are briefly described.

  • Neural representation and learning for multi-view human Action Recognition
    The 2012 International Joint Conference on Neural Networks (IJCNN), 2012
    Co-Authors: Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

    Abstract:

    In this paper we propose a novel method aiming at view-independent multi-view Action Recognition. Instead of combining the information provided by all the cameras forming the camera setup, for Action representation and classification, we perform single-view Action representation and classification to all the available videos depicting the person under consideration independently. Action representation involves a self organizing neural network training followed by fuzzy vector quantization. Action classification is performed by a feedforward neural network which is trained for view-invariant Action Recognition. Multiple Action classification results combination based on Bayesian learning, in the Recognition phase, results to high Action Recognition accuracy. The performance of the proposed Action Recognition method is evaluated on two publicly available databases, aiming at different application scenarios.

Alexandros Iosifidis – 2nd expert on this subject based on the ideXlab platform

  • Multi-view Human Action Recognition: A Survey
    2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2013
    Co-Authors: Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

    Abstract:

    While single-view human Action Recognition has attracted considerable research study in the last three decades, multi-view Action Recognition is, still, a less exploited field. This paper provides a comprehensive survey of multi-view human Action Recognition approaches. The approaches are reviewed following an application-based categorization: methods are categorized based on their ability to operate using a fixed or an arbitrary number of cameras. Finally, benchmark databases frequently used for evaluation of multi-view approaches are briefly described.

  • Neural representation and learning for multi-view human Action Recognition
    The 2012 International Joint Conference on Neural Networks (IJCNN), 2012
    Co-Authors: Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

    Abstract:

    In this paper we propose a novel method aiming at view-independent multi-view Action Recognition. Instead of combining the information provided by all the cameras forming the camera setup, for Action representation and classification, we perform single-view Action representation and classification to all the available videos depicting the person under consideration independently. Action representation involves a self organizing neural network training followed by fuzzy vector quantization. Action classification is performed by a feedforward neural network which is trained for view-invariant Action Recognition. Multiple Action classification results combination based on Bayesian learning, in the Recognition phase, results to high Action Recognition accuracy. The performance of the proposed Action Recognition method is evaluated on two publicly available databases, aiming at different application scenarios.

Anastasios Tefas – 3rd expert on this subject based on the ideXlab platform

  • Multi-view Human Action Recognition: A Survey
    2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2013
    Co-Authors: Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

    Abstract:

    While single-view human Action Recognition has attracted considerable research study in the last three decades, multi-view Action Recognition is, still, a less exploited field. This paper provides a comprehensive survey of multi-view human Action Recognition approaches. The approaches are reviewed following an application-based categorization: methods are categorized based on their ability to operate using a fixed or an arbitrary number of cameras. Finally, benchmark databases frequently used for evaluation of multi-view approaches are briefly described.

  • Neural representation and learning for multi-view human Action Recognition
    The 2012 International Joint Conference on Neural Networks (IJCNN), 2012
    Co-Authors: Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

    Abstract:

    In this paper we propose a novel method aiming at view-independent multi-view Action Recognition. Instead of combining the information provided by all the cameras forming the camera setup, for Action representation and classification, we perform single-view Action representation and classification to all the available videos depicting the person under consideration independently. Action representation involves a self organizing neural network training followed by fuzzy vector quantization. Action classification is performed by a feedforward neural network which is trained for view-invariant Action Recognition. Multiple Action classification results combination based on Bayesian learning, in the Recognition phase, results to high Action Recognition accuracy. The performance of the proposed Action Recognition method is evaluated on two publicly available databases, aiming at different application scenarios.