Human action recognition based on spatial-temporal relational model and LSTM-CNN framework

Due to the increasing popularity of human skeleton capture systems, many new methods for implementing skeleton-based action recognition has been proposed. Some of these include Long Term Memory and Convolutional Neural Networks. These methods can investigate the significant spatial-temporal information, but they are limited in their capacity to do so in real-world scenarios. In this paper, a new spatialtemporal model with a bi-temporal end-to-end framework is proposed. A novel structure is proposed to combine the functions LSTM and CNN. The structure uses the dependency model to build the skeleton data for the proposed network. Copyright (C) 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Innovation and Application in Science and Technology