research-article

End-to-end Multiplayer Violence Detection based on Deep 3D CNN

Authors:

Xue Li,

Bing WangAuthors Info & Claims

ICNCC '18: Proceedings of the 2018 VII International Conference on Network, Communication and Computing

Pages 227 - 230

https://doi.org/10.1145/3301326.3301367

Published: 14 December 2018 Publication History

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Abstract

Numerous behavior recognition researches have focused on UCF-101 video dataset, such as sports, cooking and other simple routines. Yet these studies are less useful in real-life surveillance scenarios. Violence detection in crowded scenes (such as shopping malls, banks, and stadiums) is significantly important but little research has been done. Based on this situation, this paper proposes a multiplayer violence detection method based on deep three-dimensional convolutional neural network (3D CNN), which extracts the spatiotemporal feature information of multiplayer violence. Our method directly detects violence in an input video by end-to-end. The experimental results show that the accuracy of our method is higher than the methods of artificially extracting features in violence detection.

References

[1]

Laptev, I., & Lindeberg, T. (2005). On space-time interest points. International Journal of Computer Vision, 64(2--3), 107--123.

Digital Library

Google Scholar

[2]

Lowe, D. G. (2004). Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision (Vol.60, pp.91--110).

Digital Library

Google Scholar

[3]

Chen, M. Y., Li, H., & Hauptmann, A. (2009). Informedia @ trecvid 2009: analyzing video motions., 52(11), 21--23.

Google Scholar

[4]

Xu, L., Gong, C., Yang, J., Wu, Q., & Yao, L. (2014). Violent video detection based on MoSIFT feature and sparse coding.

Crossref

Google Scholar

[5]

Hassner, T., Itcher, Y., & Kliper-Gross, O. (2012). Violent flows: Real-time detection of violent crowd behavior. Computer Vision and Pattern Recognition Workshops (pp.1--6). IEEE.

Crossref

Google Scholar

[6]

Mabrouk, A. B., & Zagrouba, E. (2017). Spatio-temporal feature using optical flow based distribution for violence detection. Pattern Recognition Letters, 92(C), 62--67.

Digital Library

Google Scholar

[7]

Ding, C., Fan, S., Zhu, M., Feng, W., & Jia, B. (2014). Violence Detection in Video by Using 3D Convolutional Neural Networks. Advances in Visual Computing. Springer International Publishing.

Google Scholar

[8]

Zhou Z, Zhu M, Yahya K. (2017). Violence Behavior Detection Based on 3D-CNN. Computer Systems & Applications.

Google Scholar

[9]

Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., & Darrell, T., et al. (2015). Long-term recurrent convolutional networks for visual recognition and description. AB initto calculation of the structures and properties of molecules /. Elsevier.

Digital Library

Google Scholar

[10]

Sudhakaran, S., & Lanz, O. (2017). Learning to detect violent videos using convolutional long short-term memory. 1--6.

Crossref

Google Scholar

[11]

Nievas, E. B., Suarez, O. D., García, G. B., & Sukthankar, R. (2011). Violence Detection in Video Using Computer Vision Techniques. International Conference on Computer Analysis of Images and Patterns (Vol.6855, pp.332--339). Springer-Verlag.

Digital Library

Google Scholar

[12]

Hassner, T., Itcher, Y., & Kliper-Gross, O. (2012). Violent flows: Real-time detection of violent crowd behavior. Computer Vision and Pattern Recognition Workshops (pp.1--6). IEEE.

Crossref

Google Scholar

[13]

Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2014). Learning spatiotemporal features with 3d convolutional networks. 4489--4497.

Digital Library

Google Scholar

[14]

Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Li, F. F. (2014). Large-Scale Video Classification with Convolutional Neural Networks. Computer Vision and Pattern Recognition (pp.1725--1732). IEEE.

Digital Library

Google Scholar

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Singh AKumar SKumar AGangrade J(2024)Violence Detection Through Deep Learning Model in SurveillanceComputation of Artificial Intelligence and Machine Learning10.1007/978-3-031-71481-8_7(86-98)Online publication date: 25-Sep-2024
https://doi.org/10.1007/978-3-031-71481-8_7
Ehsan TNahvi MMohtavipour S(2023)An accurate violence detection framework using unsupervised spatial–temporal action translation networkThe Visual Computer10.1007/s00371-023-02865-340:3(1515-1535)Online publication date: 3-May-2023
https://doi.org/10.1007/s00371-023-02865-3
Choqueluque-Roman DCamara-Chavez G(2022)Weakly Supervised Violence Detection in Surveillance VideoSensors10.3390/s2212450222:12(4502)Online publication date: 14-Jun-2022
https://doi.org/10.3390/s22124502
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Index Terms

End-to-end Multiplayer Violence Detection based on Deep 3D CNN
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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ICNCC '18: Proceedings of the 2018 VII International Conference on Network, Communication and Computing

December 2018

372 pages

ISBN:9781450365536

DOI:10.1145/3301326

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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Association for Computing Machinery

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Published: 14 December 2018

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ICNCC 2018: 2018 VII International Conference on Network, Communication and Computing

December 14 - 16, 2018

Taipei City, Taiwan

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Singh AKumar SKumar AGangrade J(2024)Violence Detection Through Deep Learning Model in SurveillanceComputation of Artificial Intelligence and Machine Learning10.1007/978-3-031-71481-8_7(86-98)Online publication date: 25-Sep-2024
https://doi.org/10.1007/978-3-031-71481-8_7
Ehsan TNahvi MMohtavipour S(2023)An accurate violence detection framework using unsupervised spatial–temporal action translation networkThe Visual Computer10.1007/s00371-023-02865-340:3(1515-1535)Online publication date: 3-May-2023
https://doi.org/10.1007/s00371-023-02865-3
Choqueluque-Roman DCamara-Chavez G(2022)Weakly Supervised Violence Detection in Surveillance VideoSensors10.3390/s2212450222:12(4502)Online publication date: 14-Jun-2022
https://doi.org/10.3390/s22124502
Zarrat Ehsan TNahvi MMehdi Mohtavipour S(2022)DABA-Net: Deep Acceleration-Based AutoEncoder Network for Violence Detection in Surveillance Cameras2022 International Conference on Machine Vision and Image Processing (MVIP)10.1109/MVIP53647.2022.9738791(1-6)Online publication date: 23-Feb-2022
https://doi.org/10.1109/MVIP53647.2022.9738791
Qi ZZhu RFu ZChai WKindratenko V(2022)Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection Model2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI56018.2022.00105(677-685)Online publication date: Oct-2022
https://doi.org/10.1109/ICTAI56018.2022.00105
Tyagi BNigam SSingh R(2022)A Review of Deep Learning Techniques for Crowd Behavior AnalysisArchives of Computational Methods in Engineering10.1007/s11831-022-09772-129:7(5427-5455)Online publication date: 23-Jun-2022
https://doi.org/10.1007/s11831-022-09772-1
Tripathi GSingh KVishwakarma D(2021)Applied convolutional neural network framework for tagging healthcare systems in crowd protest environmentMathematical Biosciences and Engineering10.3934/mbe.202143118:6(8727-8757)Online publication date: 2021
https://doi.org/10.3934/mbe.2021431
Tripathi GSingh KVishwakarma D(2020)Violence recognition using convolutional neural networkJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-20140039:5(7931-7952)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/JIFS-201400
Traoré AAkhloufi M(2020)Violence Detection in Videos using Deep Recurrent and Convolutional Neural Networks2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC42975.2020.9282971(154-159)Online publication date: 11-Oct-2020
https://dl.acm.org/doi/10.1109/SMC42975.2020.9282971
Traoré AAkhloufi M(2020)2D Bidirectional Gated Recurrent Unit Convolutional Neural Networks for End-to-End Violence Detection in VideosImage Analysis and Recognition10.1007/978-3-030-50347-5_14(152-160)Online publication date: 17-Jun-2020
https://doi.org/10.1007/978-3-030-50347-5_14
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Real Life Violence Detection in Surveillance Videos using Spatiotemporal Features

A Crowd Analysis Framework for Detecting Violence Scenes

Violence detection in videos using interest frame extraction and 3D convolutional neural network

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