| abstract |
The invention discloses a human skeleton behavior recognition method and device based on deep reinforcement learning, wherein the method includes: obtaining a video with a fixed number of frames by uniformly sampling each segment of video in a training set, and using it to train a graph convolutional neural network; After the parameters of the graph convolutional neural network are fixed, train the extraction frame network through the graph convolutional neural network to obtain representative frames that meet the preset conditions; update the graph convolutional neural network through the representative frames that meet the preset conditions; obtain the target video , and uniformly sample the target video, so that the sampled frames are sent to the frame extraction network to obtain key frames; the key frames are sent to the updated graph convolutional neural network to obtain the final category of behavior. This method can strengthen the discrimination of selected frames, remove redundant information, improve recognition performance, and reduce the amount of calculation in the test phase. At the same time, it can make full use of the topological relationship of human bones to improve the performance of behavior recognition. |