Human skeleton behavior recognition method based on end-to-end spatio-temporal graph learning neural network
A neural network and human skeleton technology, applied in the field of computer vision, can solve problems such as not considering semantic information
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[0105] The implementation method of this embodiment is as described above, and specific steps are not described in detail, and the effect is shown below only for case data. The present invention is implemented on two data sets with truth value annotations, which are:
[0106] NTU-RGB+D data set: This data set contains 37920 training skeleton sequences and 18960 test skeleton sequences;
[0107] Kinetics data set: This data set extracts 2D skeleton sequences in the Kinetics video data set, including 240,000 training skeleton sequences and 20,000 test skeleton sequences;
[0108] The main process of skeleton-based behavior recognition is as follows:
[0109] 1) Use the results of clustering for each frame to obtain the spatial node relationship of the skeleton sequence;
[0110] 2) Use the trajectory of each node to obtain the time node relationship of the skeleton sequence;
[0111] 3) Use a 10-layer graph convolutional network, where the graph input of each layer of graph convolutional ...
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