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Dangerous behaviors detection based on deep learning

Authors:

Jie SunAuthors Info & Claims

AIPR '19: Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition

Pages 24 - 27

https://doi.org/10.1145/3357254.3357267

Published: 16 August 2019 Publication History

Abstract

Deep learning has a high degree of popularity in recent years. It is widely used in computer vision, artificial intelligence and other fields. Sites with high safety needs, such as gas stations, have a high demand for monitoring of dangerous behaviors such as smoking. Under normal circumstances, gas stations will employ corresponding personnel to inspect and supervise, but such labor costs are higher, and the monitoring effect is not good. This article is to use an object detection system based on deep learning technology to detect the dangerous behavior of gas stations. This article mainly solves several problems for gas stations to detect dangerous behaviors: first, what technology is used to achieve object detection; secondly, how to increase the speed of detection as much as possible; and thirdly, how to improve the accuracy of detecting dangerous behavior. To solve the above problems, this article will introduce how to implement an object detection system based on deep learning technology. First, a data set containing dangerous goods is established, then the convolutional neural network is trained, and finally the test results of the training results are checked and transplanted. The results prove that the gas station dangerous behavior detection system based on deep learning technology realized can accurately and quickly detect dangerous objects (cigarettes, etc.) in the image.

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Cited By

Ha EKim HNa D(2024)HOD: New Harmful Object Detection Benchmarks for Robust Surveillance2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)10.1109/WACVW60836.2024.00026(183-192)Online publication date: 1-Jan-2024
https://doi.org/10.1109/WACVW60836.2024.00026
Khayrat AMalak PVictor MAhmed SMetawie HSaber VElshalakani M(2022)An intelligent Surveillance System for Detecting Abnormal Behaviors on Campus using YOLO and CNN-LSTM Networks2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)10.1109/MIUCC55081.2022.9781786(104-109)Online publication date: 8-May-2022
https://doi.org/10.1109/MIUCC55081.2022.9781786
Gong FLi YYuan XLiu XGao Y(2022)Human elbow flexion behaviour recognition based on posture estimation in complex scenesIET Image Processing10.1049/ipr2.1262617:1(178-192)Online publication date: 8-Sep-2022
https://doi.org/10.1049/ipr2.12626
Show More Cited By

Index Terms

Dangerous behaviors detection based on deep learning
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing

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AIPR '19: Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition

August 2019

198 pages

ISBN:9781450372299

DOI:10.1145/3357254

Conference Chairs:
Li Ma
North China University of Technology, China
,
Xu Huang
University of Canberra, Australia

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 August 2019

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AIPR 2019

AIPR 2019: 2019 2nd International Conference on Artificial Intelligence and Pattern Recognition

August 16 - 18, 2019

Beijing, China

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Cited By

Ha EKim HNa D(2024)HOD: New Harmful Object Detection Benchmarks for Robust Surveillance2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)10.1109/WACVW60836.2024.00026(183-192)Online publication date: 1-Jan-2024
https://doi.org/10.1109/WACVW60836.2024.00026
Khayrat AMalak PVictor MAhmed SMetawie HSaber VElshalakani M(2022)An intelligent Surveillance System for Detecting Abnormal Behaviors on Campus using YOLO and CNN-LSTM Networks2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)10.1109/MIUCC55081.2022.9781786(104-109)Online publication date: 8-May-2022
https://doi.org/10.1109/MIUCC55081.2022.9781786
Gong FLi YYuan XLiu XGao Y(2022)Human elbow flexion behaviour recognition based on posture estimation in complex scenesIET Image Processing10.1049/ipr2.1262617:1(178-192)Online publication date: 8-Sep-2022
https://doi.org/10.1049/ipr2.12626
Pundhir AVerma DKumar PRaman B(2022)Region Extraction Based Approach for Cigarette Usage Classification Using Deep LearningComputer Vision and Image Processing10.1007/978-3-031-11349-9_33(378-390)Online publication date: 24-Jul-2022
https://doi.org/10.1007/978-3-031-11349-9_33

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