The present application relates to the technical field of video image processing and artificial intelligence, and specifically relates to an environmental semantic understanding-based body movement recognition method, an apparatus, a device, and a storage medium. The method comprises: detecting a person and items included in the frame images in a video stream; performing posture recognition on the detected person included in each frame image so as to obtain the body postures; inputting the body postures into a first convolutional neural network to obtain the probabilities of different action types of the person; inputting the body postures and the items surrounding the person into a second convolutional neural network so as to obtain the probability of person falling down; outputting the movement recognition result. The present method prevents mis-recognizing an item as a person during posture recognition, and enhances the accuracy and time relevancy of body posture recognition. The second convolutional neural network uses the body postures and surrounding items to recognize a fall, thereby enhancing the accuracy of action detection, and providing good robustness to the unstable process of body posture recognition.
ENVIRONMENTAL SEMANTIC UNDERSTANDING-BASED BODY MOVEMENT RECOGNITION METHOD, APPARATUS, DEVICE, AND STORAGE MEDIUM