Abstract
In this paper, we use data from the Microsoft Kinect sensor that processes the captured image of a person, thus, reducing the number of data in just joints on each frame. Then, we propose a creation of an image from all the frames removed from the movement, which facilitates training in a convolutional neural network. Finally, we trained a CNN using two different forms of training: combined training and individual training using the MSRC-12 dataset. Thus, the trained network obtained an accuracy rate of 86.67% in combined training and 90.78% of accuracy rate in the individual training, which is a very good performance compared to related works. This demonstrates that networks based on convolutional networks can be effective for the recognition of human actions using joints.
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Pfitscher, M., Welfer, D., de Souza Leite Cuadros, M.A., Gamarra, D.F.T. (2020). Activity Gesture Recognition on Kinect Sensor Using Convolutional Neural Networks and FastDTW for the MSRC-12 Dataset. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_21
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DOI: https://doi.org/10.1007/978-3-030-16657-1_21
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