Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification

• URL: http://arxiv.org/abs/2004.08153v2
• Date: Sun, 18 Oct 2020 17:52:46 GMT
• Title: Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification
• Authors: Konstantinos Makantasis, Athanasios Voulodimos, Anastasios Doulamis, Nikolaos Bakalos, Nikolaos Doulamis
• Abstract summary: We propose a spatially and temporally aware tensor-based neural network for classification of human pose. Our model is end-to-end trainable and characterized by a small number of trainable parameters. Experimental evaluation of the proposed model indicates that it can achieve state-of-the-art performance.
• Score: 12.965269872510587
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this study, we propose a spatially and temporally aware tensor-based neural network for human pose classification using three-dimensional skeleton data. Our model employs three novel components. First, an input layer capable of constructing highly discriminative spatiotemporal features. Second, a tensor fusion operation that produces compact yet rich representations of the data, and third, a tensor-based neural network that processes data representations in their original tensor form. Our model is end-to-end trainable and characterized by a small number of trainable parameters making it suitable for problems where the annotated data is limited. Experimental evaluation of the proposed model indicates that it can achieve state-of-the-art performance.

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