Universiti Teknologi Malaysia Institutional Repository

Novel cross-view human action model recognition based on the powerful view-invariant features technique

Mambou, Sebastien and Krejcar, Ondrej and Kuca, Kamil and Selamat, Ali (2018) Novel cross-view human action model recognition based on the powerful view-invariant features technique. Future Internet, 10 (89). pp. 1-17. ISSN 1999-5903


Official URL: http://dx.doi.org/10.3390/fi10090089


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.

Item Type:Article
Uncontrolled Keywords:action recognition, cross-view actions, IXMAS, NUMA, perspective, sample-affinity matrix
Subjects:T Technology > T Technology (General)
Divisions:Malaysia-Japan International Institute of Technology
ID Code:84525
Deposited By: Yanti Mohd Shah
Deposited On:11 Jan 2020 15:32
Last Modified:11 Jan 2020 15:32

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