The invention relates to a human body action detection method adopting a recurrent neural network to judge according to a skeleton form, which comprises the following steps: collecting a video image according to a target detection requirement, and judging whether a target exists in the image; extracting a key frame from the video image; performing attitude estimation on the target according to the key frame to obtain a whole body skeleton form and a skeleton node set; partitioning and classifying the skeleton node set, determining weights of skeleton nodes respectively, and obtaining the skeleton form of the body part; performing feature fusion on the skeleton form of the body part and the skeleton form of the whole body by adopting a graph convolutional neural network; inputting the obtained fusion features into a recurrent neural network, and fusing time sequence information of previous and later moments; and inputting the image features output by the recurrent neural network into a classifier to obtain an action classification result at the current moment. According to the detection method, the influence of a background image is eliminated, the action is detected in combination with the spatio-temporal information of the key node change of the human skeleton, the recognition accuracy is improved, and the misjudgment rate is reduced.
Human body action detection method adopting recurrent neural network to judge according to skeleton form