The Experts below are selected from a list of 20280 Experts worldwide ranked by ideXlab platform
Ioannis Pitas - One of the best experts on this subject based on the ideXlab platform.
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Multi-view Human Action Recognition: A Survey
2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2013Co-Authors: Alexandros Iosifidis, Anastasios Tefas, Ioannis PitasAbstract: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.
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Neural representation and learning for multi-view human Action Recognition
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012Co-Authors: Alexandros Iosifidis, Anastasios Tefas, Ioannis PitasAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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Multi-view Human Action Recognition: A Survey
2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2013Co-Authors: Alexandros Iosifidis, Anastasios Tefas, Ioannis PitasAbstract: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.
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Neural representation and learning for multi-view human Action Recognition
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012Co-Authors: Alexandros Iosifidis, Anastasios Tefas, Ioannis PitasAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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Multi-view Human Action Recognition: A Survey
2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2013Co-Authors: Alexandros Iosifidis, Anastasios Tefas, Ioannis PitasAbstract: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.
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Neural representation and learning for multi-view human Action Recognition
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012Co-Authors: Alexandros Iosifidis, Anastasios Tefas, Ioannis PitasAbstract: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.
Piotr Koniusz - One of the best experts on this subject based on the ideXlab platform.
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A Comparative Review of Recent Kinect-Based Action Recognition Algorithms
IEEE Transactions on Image Processing, 2020Co-Authors: Lei Wang, Du Q. Huynh, Piotr KoniuszAbstract:Video-based human Action Recognition is currently one of the most active research areas in computer vision. Various research studies indicate that the performance of Action Recognition is highly dependent on the type of features being extracted and how the Actions are represented. Since the release of the Kinect camera, a large number of Kinect-based human Action Recognition techniques have been proposed in the literature. However, there still does not exist a thorough comparison of these Kinect-based techniques under the grouping of feature types, such as handcrafted versus deep learning features and depth-based versus skeleton-based features. In this paper, we analyze and compare 10 recent Kinect-based algorithms for both cross-subject Action Recognition and cross-view Action Recognition using six benchmark datasets. In addition, we have implemented and improved some of these techniques and included their variants in the comparison. Our experiments show that the majority of methods perform better on cross-subject Action Recognition than cross-view Action Recognition, that the skeleton-based features are more robust for cross-view Recognition than the depth-based features, and that the deep learning features are suitable for large datasets.
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A Comparative Review of Recent Kinect-based Action Recognition Algorithms.
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Lei Wang, Du Q. Huynh, Piotr KoniuszAbstract:Video-based human Action Recognition is currently one of the most active research areas in computer vision. Various research studies indicate that the performance of Action Recognition is highly dependent on the type of features being extracted and how the Actions are represented. Since the release of the Kinect camera, a large number of Kinect-based human Action Recognition techniques have been proposed in the literature. However, there still does not exist a thorough comparison of these Kinect-based techniques under the grouping of feature types, such as handcrafted versus deep learning features and depth-based versus skeleton-based features. In this paper, we analyze and compare ten recent Kinect-based algorithms for both cross-subject Action Recognition and cross-view Action Recognition using six benchmark datasets. In addition, we have implemented and improved some of these techniques and included their variants in the comparison. Our experiments show that the majority of methods perform better on cross-subject Action Recognition than cross-view Action Recognition, that skeleton-based features are more robust for cross-view Recognition than depth-based features, and that deep learning features are suitable for large datasets.
Arjan Kuijper - One of the best experts on this subject based on the ideXlab platform.
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Human Action Recognition based on skeleton splitting
Expert Systems With Applications, 2013Co-Authors: Sang Min Yoon, Arjan KuijperAbstract:Human Action Recognition, defined as the understanding of the human basic Actions from video streams, has a long history in the area of computer vision and pattern Recognition because it can be used for various applications. We propose a novel human Action Recognition methodology by extracting the human skeletal features and separating them into several human body parts such as face, torso, and limbs to efficiently visualize and analyze the motion of human body parts. Our proposed human Action Recognition system consists of two steps: (i) automatic skeletal feature extrAction and splitting by measuring the similarity between neighbor pixels in the space of diffusion tensor fields, and (ii) human Action Recognition by using multiple kernel based Support Vector Machine. Experimental results on a set of test database show that our proposed method is very efficient and effective to recognize the Actions using few parameters.
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Human Action Recognition based on skeleton splitting
Expert Systems with Applications, 2013Co-Authors: Sang Min Yoon, Arjan KuijperAbstract:Human Action Recognition, defined as the understanding of the human basic Actions from video streams, has a long history in the area of computer vision and pattern Recognition because it can be used for various applications. We propose a novel human Action Recognition methodology by extracting the human skeletal features and separating them into several human body parts such as face, torso, and limbs to efficiently visualize and analyze the motion of human body parts. Our proposed human Action Recognition system consists of two steps: (i) automatic skeletal feature extrAction and splitting by measuring the similarity between neighbor pixels in the space of diffusion tensor fields, and (ii) human Action Recognition by using multiple kernel based Support Vector Machine. Experimental results on a set of test database show that our proposed method is very efficient and effective to recognize the Actions using few parameters. ?? 2013 Elsevier Ltd. All rights reserved.
