Research on 3D convolutional autoencoder enhanced metro abnormal behavior detection based on multi-level attentional memory

Subway is one of the most important rail transit tools in China, which has the advantages of convenience, safety and high efficiency, and subway has also become the main means of transportation for people to travel. However, the subway scene has the characteristics of narrow space and large passenge...

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Vydané v:Multimedia tools and applications Ročník 84; číslo 21; s. 23861 - 23879
Hlavní autori: Ye, Run, Zhang, Kun, Zhang, Cheng, Yan, Bin, Zhou, Xiaojia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.06.2025
Springer Nature B.V
Predmet:
Algorithms
Coders
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Datasets
Deep learning
Energy consumption
Methods
Multimedia Information Systems
Neural networks
Passengers
Pedestrians
Semantics
Special Purpose and Application-Based Systems
Subways
Surveillance
Time series
Track 6: Computer Vision for Multimedia Applications
Deep learning
Attention mechanism
Pedestrian abnormal behavior detection
Convolution self-encoder
Subway scene
ISSN:1573-7721, 1380-7501, 1573-7721
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Popis
Shrnutí:Subway is one of the most important rail transit tools in China, which has the advantages of convenience, safety and high efficiency, and subway has also become the main means of transportation for people to travel. However, the subway scene has the characteristics of narrow space and large passenger flow, and abnormal behavior events often occur in the subway scene during passenger rush hours. At present, the abnormal behavior detection methods of manual monitoring in subway scenes have been unable to meet the increasing demand of passenger traffic. In this paper, pedestrian abnormal behavior detection in subway scene is studied, and a self-encoder abnormal behavior detection method based on channel attention mechanism and multi-level memory enhancement is proposed. It solves the problems of excessive generalization ability of traditional convolutional self-encoders and difficulties in extracting pedestrian behavior features under complex subway background, which is verified by experiments. Finally, better performance has been achieved in UCSDPed2 dataset, CUHK Avenue dataset and Chengdu Metro self-built dataset.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-024-20061-w