Restaurant kitchen personnel behavior identification method based on attention mechanism

Abstract

The invention discloses a restaurant kitchen personnel behavior identification method based on an attention mechanism, and aims to effectively identify human body behaviors in restaurant kitchen videos and realize kitchen monitoring management. The method comprises the following steps: acquiring working videos of kitchen workers by using a camera, constructing a database of which the videos comprise clear actions, performing frame cutting on the database, and extracting optical flow images; inputting the video frames into a spatial stream network based on a spatial attention mechanism to obtain spatial features; inputting the optical flow image into a time flow network based on a long-short-term memory network to obtain a time sequence feature; and respectively inputting the space and timesequence features into a classifier to obtain classification scores, and carrying out score fusion to complete kitchen personnel behavior identification. According to the method, a spatial attentionmechanism is added, so that the model pays more attention to more important points in space; and the long-short-term memory network better reserves the time sequence information in the video, so thatthe kitchen personnel behavior recognition accuracy is improved.

Description

technical field [0001] The invention relates to the fields of image understanding and computer vision, in particular to a method for recognizing human body behavior. Background technique [0002] With the rapid development of economy and technology, the demand for video surveillance systems continues to increase, such as parking lots, supermarkets, shopping malls, banks, factories, mines, restaurant kitchens and other places. Mining human behavior information in videos has become a major development direction. [0003] To put it simply, behavior recognition is to classify a given video clip. The categories are usually various actions of people, that is, to detect the behavior of the human body and better grasp the behavior information of people in the video. Nowadays, bright kitchens and bright stoves are getting more and more attention. There is an increasing demand to detect whether there are violations of human behavior in the video of the restaurant’s back kitchen. Real...

AI Technical Summary

Problems solved by technology

Compared with 2D convolution, 3D convolutional network can better capture spatiotemporal information, but it requires expensive computing consumption and storage requirements

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