ENVIRONMENTAL SEMANTIC UNDERSTANDING-BASED BODY MOVEMENT RECOGNITION METHOD, APPARATUS, DEVICE, AND STORAGE MEDIUM

摘要

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.