Abstract：The existing method of human action recognition was to divide event data as virtual frames with fixed time interval. However, these technologies did not take full advantage of the asynchronous output feature of the event camera. In order to solve the shortages, a method to deal with event data directly is proposed. First, event camera is used to obtain nine kinds of common human action data, then preprocessing include filtering and grid down sampling. The preprocess can not only remove noise but also reduce the amount of input data of the model. Second, the shared convolution is used to extract the action space features, which use parallel convolution operation. Finally, human actions are classified and recognized. The experimental results show that the average recognition accuracy is 91.3% under normal illumination, different illumination has little effect on the accuracy of this method, it also has the advantages of fast training time and small number of parameters.
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2022, Vol. 43 
