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A. Papadakis, E. Mathe, E. Spyrou, Ph. Mylonas
A Geometric Approach for Cross-View Human Action Recognition using Deep Learning
11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019), 23-25 September 2019, Dubrovnik, Croatia
ABSTRACT
In this paper we present an approach for the recognition of human actions which is based on a deep Convolutional Neural Network architecture. More specifically, 3D skeletal joint information is used to create 2D (image) representations. To compensate for potential viewpoint changes, these images are pre-processed using geometric transformations. Then, they are transformed to the spectral domain using well-known transforms. We focus on actions that are close to activities of daily living (ADLs), yet we evaluate our approach using a large-scale action dataset. We cover single-view, cross-view and cross subject cases and thoroughly discuss experimental results and the potential of our approach.
23 September, 2019
A. Papadakis, E. Mathe, E. Spyrou, Ph. Mylonas, "A Geometric Approach for Cross-View Human Action Recognition using Deep Learning", 11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019), 23-25 September 2019, Dubrovnik, Croatia
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