Patent ID: 11928893
Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 16:
17. The apparatus according to claim 11, wherein the one or more processors are further configured to:
obtain a training video sample, the training video sample comprising a plurality of different sample temporal frames and standard action types of a moving object in the sample temporal frames;
perform a training process comprising:
obtaining original feature submap samples of each of the sample temporal frames on the different convolutional channels by using the multi-channel convolutional layer;
obtaining, by using each of the sample temporal frames as a target sample temporal frame, sample difference information between original feature submap samples of the target sample temporal frame and original feature submap samples of a next sample temporal frame on the convolutional channels;
mapping the sample difference information on the convolutional channels into motion information weight samples of the target sample temporal frame on the convolutional channels by using the activation function;
obtaining motion information feature map samples of the target sample temporal frame on the convolutional channels according to the motion information weight samples and the original feature submap samples of the target sample temporal frame on the convolutional channels;
performing temporal convolution on the motion information feature map samples of the target sample temporal frame on the convolutional channels, to obtain temporal motion feature map samples of the target sample temporal frame on the convolutional channels;
obtaining a predicted action type of the moving object in an image sample of the target sample temporal frame according to the temporal motion feature map samples of the target sample temporal frame on the convolutional channels; and
adjusting parameters of the multi-channel convolutional layer, the activation function, and a temporal convolution kernel according to a difference between the predicted action type and a standard action type of the target sample temporal frame, and continue the training process until a training ending condition is met.