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Future Internet 2018, 10(9), 89;

Novel Cross-View Human Action Model Recognition Based on the Powerful View-Invariant Features Technique

Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, 500 03 Hradec Kralove, Czech Republic
School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM) & Media and Games Centre of Excellence (MagicX), UTM Johor Baharu 81310, Malaysia
Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
Author to whom correspondence should be addressed.
Received: 19 July 2018 / Revised: 9 September 2018 / Accepted: 12 September 2018 / Published: 13 September 2018
PDF [2530 KB, uploaded 13 September 2018]


One of the most important research topics nowadays is human action recognition, which is of significant interest to the computer vision and machine learning communities. Some of the factors that hamper it include changes in postures and shapes and the memory space and time required to gather, store, label, and process the pictures. During our research, we noted a considerable complexity to recognize human actions from different viewpoints, and this can be explained by the position and orientation of the viewer related to the position of the subject. We attempted to address this issue in this paper by learning different special view-invariant facets that are robust to view variations. Moreover, we focused on providing a solution to this challenge by exploring view-specific as well as view-shared facets utilizing a novel deep model called the sample-affinity matrix (SAM). These models can accurately determine the similarities among samples of videos in diverse angles of the camera and enable us to precisely fine-tune transfer between various views and learn more detailed shared facets found in cross-view action identification. Additionally, we proposed a novel view-invariant facets algorithm that enabled us to better comprehend the internal processes of our project. Using a series of experiments applied on INRIA Xmas Motion Acquisition Sequences (IXMAS) and the Northwestern–UCLA Multi-view Action 3D (NUMA) datasets, we were able to show that our technique performs much better than state-of-the-art techniques. View Full-Text
Keywords: action recognition; perspective; sample-affinity matrix; cross-view actions; NUMA; IXMAS action recognition; perspective; sample-affinity matrix; cross-view actions; NUMA; IXMAS

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Mambou, S.; Krejcar, O.; Kuca, K.; Selamat, A. Novel Cross-View Human Action Model Recognition Based on the Powerful View-Invariant Features Technique. Future Internet 2018, 10, 89.

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