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In order to solve the problem of insufficient feature expression ability in gesture recognition in emergency rescue, this paper proposes a multi-feature fusion dynamic gesture recognition network (MGN-LSTM). The model first extracts the global information and local information of each granularity through a Multiple Granularity Network (MGN). Then, a layer of Long Short-Term Memory network (LSTM) is used to extract the time information of gestures. Finally, in the model prediction stage, the candy algorithm is added to detect the edge feature information of the gesture, and the recognition result is obtained by combining the global, local and time information. The experimental results show that the multi-feature fusion method proposed in this paper has good feature expression ability and has been verified in both the self-built emergency rescue gesture dataset and the public dataset IsoGD, which improves the accuracy of gesture recognition.
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