| S. Stylianou-Nikolaidou, I. Vernikos, E. Mathe, E. Spyrou, Ph. Mylonas |
| A Novel CNN-LSTM Hybrid Architecture for the Recognition of Human Activities |
| 22nd International Conference on Engineering Applications of Neural Networks, EANN 2021, 25 - 27 June 2021 |
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ABSTRACT
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| The problem of human activity recognition (HAR) has been increasingly attracting the efforts of the research community, having several applications. In this paper we propose a multi-modal approach addressing the task of video-based HAR. Our approach uses three modalities, i.e., raw RGB video data, depth sequences and 3D skeletal motion data. The latter are transformed into a 2D image representation into the spectral domain. In order to extract spatio-temporal features from the available data, we propose a novel hybrid deep neural network architecture that combines a Convolutional Neural Network (CNN) and a Long-Short Term Memory (LSTM) network. We focus on the tasks of recognition of activities of daily living (ADLs) and medical conditions and we evaluate our approach using two challenging, publicly available, multi-view datasets.
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| 25 June , 2021 |
| S. Stylianou-Nikolaidou, I. Vernikos, E. Mathe, E. Spyrou, Ph. Mylonas, "A Novel CNN-LSTM Hybrid Architecture for the Recognition of Human Activities", 22nd International Conference on Engineering Applications of Neural Networks, EANN 2021, 25 - 27 June 2021 |
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