Viewpoint adjustment-based graph convolution cycle network skeleton action recognition method and system
一种动作识别、循环神经网络的技术,应用在基于视点调整的图卷积循环网络骨骼动作识别领域,能够解决观测视角不同、识别结果不同、动作识别准确率低等问题,达到广阔应用前景、提高准确率的效果
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Embodiment 1
[0029] Such as figure 1 As shown, Embodiment 1 of the present disclosure provides a skeletal action recognition method based on a viewpoint adjustment based on a graph convolutional loop network, including the following steps:
[0030] (1) Preprocess the acquired action data and use the NTU-RGB+D dataset as the action recognition dataset. This dataset is the largest action data and provides 3D bone coordinates, including 60 different actions, including The two benchmarks of cross-perspective and cross-subject;
[0031] Specifically:
[0032] (1-1) Obtain the original body data from the skeleton sequence; obtain the original body data from the skeleton sequence, each body data is a dictionary, including keywords such as the original 3D joint, the original 2D color position, and the frame index of the subject;
[0033] (1-2) Obtain denoising data from the original skeleton sequence; obtain denoising data (joint positions and color positions) from the original skeleton sequence...
Embodiment 2
[0055] Embodiment 2 of the present disclosure provides a skeletal action recognition system based on viewpoint adjustment based on graph convolutional loop network, including:
[0056] The preprocessing module is configured to: preprocess the acquired action data;
[0057] The bone data prediction module is configured to: use the trained graph convolutional cyclic neural network and use the preprocessed data as input to obtain the spatiotemporal information of the bone data;
[0058] The classification module is configured to: use the Softmax function and take the obtained spatio-temporal information as input to obtain a classification result of the skeletal motion.
[0059] The specific identification method is the same as that in Embodiment 1, and will not be repeated here.
Embodiment 3
[0061] Embodiment 3 of the present disclosure provides a medium on which a program is stored, and when the program is executed by a processor, the steps in the viewpoint-adjusted graph convolutional loop network skeleton action recognition method based on viewpoint adjustment as described in Embodiment 1 of the present disclosure are implemented. .
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