Behavior recognition method and device and storage medium

Abstract

The invention discloses a behavior recognition method and device and a storage medium. According to the scheme, a to-be-detected video is acquired, and a plurality of candidate windows are added to the to-be-detected video; based on the feature extraction network, generating a three-dimensional feature map of the to-be-detected video containing a plurality of candidate windows on a plurality of time domain scales; determining a time domain scale matched with the video clip in the candidate window, obtaining a three-dimensional feature map corresponding to the determined time domain scale, andobtaining a local feature map corresponding to the video clip according to the obtained three-dimensional feature map; and performing behavior recognition according to the local feature map and a preset behavior recognition network, and determining a behavior category corresponding to the behavior feature in the video clip. According to the scheme, the three-dimensional feature maps of the to-be-detected video on multiple time domain scales can be obtained from the to-be-detected video by using the feature extraction network, so that the receptive field of the classifier can adapt to the behavior features of different time lengths, and the accuracy of behavior recognition of multiple time spans is improved.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a behavior recognition method, device and storage medium. Background technique [0002] With the increasing demand for computer intelligence and the rapid development of pattern recognition technology, image processing technology and artificial intelligence technology, there is a huge practical demand for using computer vision technology to analyze video content, such as detecting human behavior in videos. In the prior art, the hierarchical structure of the neural network is used to learn complex and diverse feature patterns from the training data, so as to effectively extract the features of the input video and identify specific behaviors. [0003] In practical applications, the vast majority of surveillance videos and network videos are undivided long videos, and long videos may contain multiple behavior instances, and the duration of each behavior instance may ...

AI Technical Summary

Problems solved by technology

[0003] In practical applications, the vast majority of surveillance videos and network videos are undivided long videos, and long videos may contain multiple behavior instances, and the duration of each behavior instance may be different. However, the existing behavior recognition In the solution, it is generally necessary to compress or expand the video into a video segmen

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