Crowd Gathering Abnormal Behavior Detection Based on Convolutional Neural Network: Crowd Gathering Abnormal Behavior Detection Based on Convolutional Neural Network
Authors: 
Xingya Yan, Kaiwen JiangAuthors Info & Claims
Pages 224 - 230
Abstract
In order to detect abnormal crowd gathering behavior, a method based on convolutional neural network is proposed. For individuals in the crowd, use the improved multi-scale convolutional neural network (MCNN) to predict and extract the head coordinate points of each pedestrian individual in each frame of the video image to calculate the number of people and crowd density, which is more effective than traditional detection methods. There is less occlusion of pedestrians' heads in the crowd, which solves the problem of detection of occluded human bodies in traditional algorithms. According to the extracted coordinate points, calculate the crowd average kinetic energy, crowd density value and crowd distribution entropy, which are three crowd movement state characteristic values, so as to better judge crowd gathering behavior.
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Published In
September 2023
1540 pages
ISBN:9798400707674
DOI:10.1145/3641584
Copyright © 2023 ACM.
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Published: 14 June 2024
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AIPR 2023
AIPR 2023: 2023 6th International Conference on Artificial Intelligence and Pattern Recognition
September 22 - 24, 2023
Xiamen, China
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