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A time sequence location method of long video violence based on improved C3D network

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A Correction to this article was published on 12 July 2022

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Abstract

This paper mainly studies the retrieval and location of violence in long time sequence video. Aiming at the low accuracy of violence detection in long time sequence video, a two-stage violence time sequence location method based on DC3D network model is proposed in this paper. In the video preprocessing stage, this paper adopts the method of generating video at multiple scales. In the first stage, we first use a large number of labeled datasets for pre-training to obtain a C3D network model for generating candidate videos and finally filter out meaningless background videos. In the second stage, we use the deconvolution method to identify the candidate video accurately to the frame level, so as to determine the specific time of violence. In the first stage, this method can improve the overall accuracy by generating candidate videos. DC3D network model can accurately locate the time of violence to the level of frame. The experimental results show that the proposed method can quickly retrieve and locate the violent fighting behavior in the surveillance video under the long time sequence video. The research results of this paper can provide convenience for the surveillance personnel to quickly retrieve and locate the target segment in a large amount of video data.

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Data availability

The authors declare that all the experimental data in this paper are true, valid and available. The authors declare that all experimental data are obtained from detailed experiments.

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Funding

This paper is supported by Special Fund for Science and Technology Innovation Strategy of Guangdong Province in 2021 (Special Fund for Climbing Plan) (pdjh2021a0944), Special Projects in Key Fields of Colleges and Universities in Guangdong Province in 2021(2021ZDZX1093), Dongguan Science and Technology of Social Development Program in 2021 (20211800900252), Special fund for electronic information engineering technology specialty group of national double high program of Dongguan Polytechnic in 2021 (ZXYYD001), Special fund for electronic information engineering technology specialty group of national double high program of Dongguan Polytechnic in 2021 (ZXF002), Special fund for electronic information engineering technology specialty group of national double high program of Dongguan Polytechnic in 2022 (ZXB202203), Special fund for electronic information engineering technology specialty group of national double high program of Dongguan Polytechnic in 2022 (ZXC202201) and Special fund for electronic information engineering technology specialty group of national double high program of Dongguan Polytechnic in 2022 (ZXD202204).

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Authors and Affiliations

  1. Department of Information Engineering, Gannan University of Science and Technology, Ganzhou, 341000, Jiangxi, China

    Wen Qu & Ting Zhu

  2. School of Electronic Information, Dongguan Polytechnic, Dongguan, 523808, China

    Jie Liu & Jianxin Li

Authors
  1. Wen Qu
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  2. Ting Zhu
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  3. Jie Liu
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  4. Jianxin Li
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Contributions

WQ was involved in the conceptualization, methodology, validation, investigation, writing and funding acquisition. TZ contributed to the formal analysis, software, resources and visualization. JL contributed to the software, resources, methodology, validation, writing—review and editing, supervision and funding acquisition. JL helped in the data curation, formal analysis, validation,writing and funding acquisition.

Corresponding author

Correspondence to Ting Zhu.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Any participants (or their guardians if unable to give informed consent, or next of kin, if deceased) who may be identifiable through the manuscript (such as a case report) have been given an opportunity to review the final manuscript and have provided written consent to publish.

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The original online version of this article was revised: In this article the affiliation details for author Ting Zhu were incorrectly assigned.

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Qu, W., Zhu, T., Liu, J. et al. A time sequence location method of long video violence based on improved C3D network. J Supercomput 78, 19545–19565 (2022). https://doi.org/10.1007/s11227-022-04649-3

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  • DOI: https://doi.org/10.1007/s11227-022-04649-3

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