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

A deep neural network and anomaly detection technology, which is applied in the field of abnormal detection and positioning of crowd emotions based on deep neural network, can solve problems such as detection limitations, achieve the effects of avoiding detection limitations, reasonable design, and improving accuracy

Active Publication Date: 2017-09-15
GUANGDONG UNIV OF TECH
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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 s

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

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Embodiment Construction

[0046] The specific embodiment of the present invention will be further described below in conjunction with accompanying drawing:

[0047] Such as figure 1 As shown, a method for abnormal detection and location of crowd emotions based on deep neural network is characterized in that it comprises the following steps:

[0048] S1), data acquisition: acquire video data through monitoring equipment as training video data;

[0049] S2, using video key frame extraction technology to extract video key frame data from training video data;

[0050] S3), data processing: use face detection technology to obtain the face image data of each frame from the video key frame data, and detect the face feature points in the face image, after aligning the face images according to the face feature points, Group the face images according to different individuals, and sort the grouped face image data according to the sequence of video key frames;

[0051] S4), construction of face emotion recognitio...

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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...

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Application Information

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IPC IPC(8): G06K9/00G06N3/02
CPCG06N3/02G06V40/161G06V40/168G06V20/40
Inventor 郝志峰郑小宾蔡瑞初温雯王丽娟陈炳丰
Owner GUANGDONG UNIV OF TECH
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