Crowd emotion anomaly detection and positioning method based on deep neural network

Abstract

The invention relates to a crowd emotion anomaly detection and positioning method based on a deep neural network. Video data is obtained through a monitoring device. Video key frame data is extracted from the video data. Data of each face image is obtained from the video key frame data. Alignment, grouping and sorting preprocessing are carried out. The data is input into a trained face emotion identification model based on a convolutional neural network. Crowd emotion anomaly detection and positioning results in monitored video data is obtained and is fed back to a worker of the monitoring device through a trained crowd emotion detection and positioning model. The method is reasonable in design; a relationship between a crowd emotion anomaly and a crowd anomaly can be obtained through the model, the detection limitation problem resulting from associating the crowd anomaly with a specific anomaly event is avoided, and moreover, the model is a hybrid deep neural network structure model, so the video crowd emotion anomaly detection and positioning efficiency is further improved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a method for detecting and locating crowd emotional anomalies based on a deep neural network. Background technique [0002] With the continuous and stable development of society and the continuous improvement of people's material and cultural living standards, entertainment activities and commercial activities in urban crowd gathering areas have attracted more and more public attention. These activities often have limited venue space and a large number of participants. It is very easy to cause serious harm to the lives and property safety of the general public. In order to detect abnormal situations as early as possible and take timely measures, we mainly rely on the monitoring equipment widely present in the city to detect and locate abnormal situations through monitoring equipment; [0003] Anomaly detection is mainly divided into two categories: overall anomaly detecti...

AI Technical Summary

Problems solved by technology

[0008] Aiming at the deficiencies of the prior art, the present invention provides a method for detecting and locating abnormal crowd emotions based on a deep neural network, so as to solve the detection limitations caused by the direct correlation between the abnormal definition and specific abnormal events in the existing crowd abnormal detection technology question

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