Examination room cheating behavior analysis method based on motion feature enhancement and long time sequence modeling

Abstract

The invention discloses an examination room cheating behavior analysis method based on motion feature enhancement and long time sequence modeling, and belongs to the video behavior recognition field and the deep learning field. The method comprises the steps: collecting a data set, carrying out the behavior type marking of the data, extracting a video stream as an image frame, improving the capturing capability of a model on a moving target based on a motion feature enhancement method, carrying out information fusion between frames through a feature spectrum shift mode, carrying out modeling on a long-time sequential relationship based on a sequential pyramid method, and completing the construction of an identification model; secondly, initializing a behavior recognition classification model by adopting a Xavier method according to an image obtained by the data set, obtaining a sampling sequence of video frames by adopting a segmented extraction mode, carrying out iteration to a preset number of iterations based on a loss function of the classification model, and completing training of the model; and finally, using a video frame sequence obtained through sampling for reasoning testing, and obtaining a specific behavior category result.

Description

technical field [0001] The invention belongs to the field of behavior recognition and deep learning, and relates to a method for analyzing cheating behavior in an examination room based on motion feature enhancement and long-term sequence modeling. Background technique [0002] Behavior recognition is a research field that has attracted much attention in the field of computer vision. Its purpose is to identify the behavior category of the current person in the video, so it is considered to be an important basis for video understanding. In recent years, with the improvement of computer computing power, the development of deep convolutional neural network has made many remarkable achievements in video behavior recognition tasks. [0003] Nowadays, the behavior recognition models with superior performance are all based on deep convolutional neural network, and its powerful feature extraction ability has brought great development to various tasks of computer vision. Compared wi...

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Problems solved by technology

[0006] The purpose of the present invention is to: provide a method for analyzing cheating behavior in an examination room based on motion feature enhancement and long-term time series modeling, and solve the problem that in samples dominated by motion information, motion features will have an important impact on the results of behavior recognition models, and Static feature information basically does not contribute to the improvement of recognition accuracy. The current general-purpose model cannot directly emphasize motion-related features from the feature spectrum of RGB frame sequences and suppress scene features, resulting in low accuracy.

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