research-article

Human Fall Detection Model with Lightweight Network and Tracking in Video

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Yanrong YangAuthors Info & Claims

CSAI '21: Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence

Pages 1 - 7

https://doi.org/10.1145/3507548.3507549

Published: 09 March 2022 Publication History

Abstract

In order to real time and accurately detect the action of human falling, combined with lightweight detection network, Kalman filter tracking, posture estimation network and spatiotemporal graph convolutional network, a joint algorithm for human fall detection in video is proposed. Firstly, the lightweight YOLOv3-Tiny algorithm is used to locate the frame of human in video, which can quickly detect the human-frame; among them, for the situation that the human body is likely to be missed in video, the Kalman filter tracking algorithm is integrated into the stage of target-detection and the accuracy of detecting is improved. Secondly, the human-frame detected or tracked in video is sent to the AlphaPose network to estimate the posture graph about human body. Finally, the spatiotemporal graph convolutional network is exploited to extract the spatiotemporal features of the human body, and eventually the result for classification is output. Experimental results show that the algorithm proposed in this paper, which is more appealing and successful than the other algorithm.

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Cited By

Paulauskaite-Taraseviciene ASiaulys JSutiene KPetravicius TNavickas SOliandra MRapalis ABalciunas J(2023)Geriatric Care Management System Powered by the IoT and Computer Vision TechniquesHealthcare10.3390/healthcare1108115211:8(1152)Online publication date: 17-Apr-2023
https://doi.org/10.3390/healthcare11081152
Cam NVan Nhinh NTrang T(2022)Fall Detection System Based on Pose Estimation in VideosIntelligent Computing & Optimization10.1007/978-3-031-19958-5_16(162-172)Online publication date: 21-Oct-2022
https://doi.org/10.1007/978-3-031-19958-5_16

Index Terms

Human Fall Detection Model with Lightweight Network and Tracking in Video
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

CSAI '21: Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence

December 2021

437 pages

ISBN:9781450384155

DOI:10.1145/3507548

Copyright © 2021 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 ACM 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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Published: 09 March 2022

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CSAI 2021

CSAI 2021: 2021 5th International Conference on Computer Science and Artificial Intelligence

December 4 - 6, 2021

Beijing, China

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Cited By

Paulauskaite-Taraseviciene ASiaulys JSutiene KPetravicius TNavickas SOliandra MRapalis ABalciunas J(2023)Geriatric Care Management System Powered by the IoT and Computer Vision TechniquesHealthcare10.3390/healthcare1108115211:8(1152)Online publication date: 17-Apr-2023
https://doi.org/10.3390/healthcare11081152
Cam NVan Nhinh NTrang T(2022)Fall Detection System Based on Pose Estimation in VideosIntelligent Computing & Optimization10.1007/978-3-031-19958-5_16(162-172)Online publication date: 21-Oct-2022
https://doi.org/10.1007/978-3-031-19958-5_16

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