A method and system for video behavior recognition based on spatial enhancement module

Abstract

The invention discloses a video behavior recognition method and system based on a spatial enhancement module. The method includes the following steps: decoding the video to be tested into a frame sequence, storing the decoded frame sequence in the form of an image; taking sparse sampling Strategy, divide the video into multiple video clips, extract a frame from each video clip, and combine them into a stacked frame sequence; calculate the mean value of the three channels of all training video frames in the behavior recognition data set, and divide the sampled frames Subtract the calculated mean value from the image; use the residual neural network 3D-ResNet-18 as the backbone network, and construct the behavior recognition and classification network with the space enhancement module; set the training parameters, input the training set into the behavior recognition and classification network for training, and save the training Network parameters; in the model deployment stage, the linear operator of the fusion space enhancement module; the video to be tested is input to the behavior recognition classification network, and the final classification result is output. The invention improves the behavior recognition effect, and has both effectiveness and versatility.

Description

technical field [0001] The invention relates to the technical field of video behavior recognition of computer vision, in particular to a method and system for video behavior recognition based on a spatial enhancement module. Background technique [0002] Human behavior recognition has always been a key research issue in the field of computer vision. Through the study of human body posture and behavior, abnormal behaviors in public places can be detected, such as: running fast, falling, hitting people, etc. Applying deep learning theory to computer vision applications can design high-precision and high-efficiency behavior recognition algorithms. A highly practical behavior recognition algorithm has played an important role in guaranteeing public safety, and it also enables the computer to automatically complete the detection of abnormal human behaviors, saving the time for managers to search manually. The purpose of video behavior recognition is to identify the specific cat...

AI Technical Summary

Problems solved by technology

However, its joint learning of spatio-temporal features leads to a significant attenuation in the ability to extract spatial features alone compared with 2D convolution, so the classification accuracy on data sets with strong spatial features and weak timing is weaker than that of 2D convolution.

Images