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A Group Emotion Recognition Method Based on Motion Features

A motion feature and emotion recognition technology, applied in character and pattern recognition, instruments, calculations, etc., can solve problems such as single features and insufficient performance analysis, to solve the problem of low accuracy, improve network speed and computing efficiency, and improve performance effect

Active Publication Date: 2022-07-01
SICHUAN UNIV
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Problems solved by technology

However, the features extracted by these traditional algorithms are usually too single, resulting in insufficient in-depth analysis of performance.

Method used

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  • A Group Emotion Recognition Method Based on Motion Features
  • A Group Emotion Recognition Method Based on Motion Features

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Embodiment Construction

[0027] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is necessary to point out that the following embodiments are only used to further illustrate the present invention, and should not be construed as limiting the scope of protection of the present invention. Those skilled in the art are familiar with the present invention. According to the above-mentioned content of the invention, some non-essential improvements and adjustments are made to the present invention for specific implementation, which should still belong to the protection scope of the present invention.

[0028] The group emotion recognition method based on motion features specifically includes the following steps:

[0029] (1) Use a hybrid dataset that combines the CUHK population dataset, UCF dataset, Web dataset, and PET2009 dataset, and divide each long video in the dataset into 4 short videos. The 4-frame group is divided into m...

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Abstract

The invention provides a group emotion recognition method based on motion characteristics, which mainly involves analyzing the emotion in a scene video sequence by using a multi-channel group emotion recognition network. The method includes: constructing a multi-channel group emotion recognition network, using the network to extract low-level motion features of different time series in parallel, rearranging and merging the low-level features extracted by each channel in the time dimension, and obtaining global high-level features through a 3D residual module, Realize group emotion recognition. The present invention effectively avoids the problems such as deviation and time-consuming of manual feature extraction, so that its adaptability is stronger. In addition, the multi-channel network is used for feature extraction of long video sequences in time series, which fully considers the temporal correlation between frames, and the low-level time series features are rearranged and fused in the time dimension to reduce the coupling between features. Improve the accuracy and efficiency of group emotion recognition.

Description

technical field [0001] The invention relates to the problem of emotion recognition in the field of deep learning, in particular to a group emotion recognition method based on motion characteristics. Background technique [0002] Crowd sentiment analysis is to judge the emotional state of the crowd by analyzing the crowd's behavior, clothing, etc. A large number of videos exist in real life, such as drone video surveillance, network sharing video, 3D video and so on. By analyzing the emotions of the crowd in the video, it will help to dynamically understand the emotions and changes of the crowd in the video, and has a broad application prospect. [0003] Group emotion recognition mainly analyzes the crowd emotion in the scene when the target is closer to the camera. However, in the new era of rapid development, only analyzing clearly visible faces and group emotions can no longer fully satisfy the perception of people's emotional states. Therefore, we not only need to upgr...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/52G06V10/764G06K9/62
CPCG06V20/53G06F18/241
Inventor 卿粼波许盛宇吴晓红何小海滕奇志周文俊
Owner SICHUAN UNIV
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