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- W4385800510 abstract "<p> Detection of violence and weaponized violence in closed-circuit television (CCTV) footage requires a comprehensive approach. In this work, we introduce the emph{Smart-City CCTV Violence Detection (SCVD)} dataset, specifically designed to facilitate the learning of weapon distribution in surveillance videos. To tackle the complexities of analyzing 3D surveillance video for violence recognition tasks, we propose a novel technique called, emph{SSIVD-Net} (textbf{S}alient-textbf{S}uper-textbf{I}mage for textbf{V}iolence textbf{D}etection). Our method reduces 3D video data complexity, dimensionality, and information loss while improving inference, performance, and explainability through the use of Salient-Super-Image representations. Considering the scalability and sustainability requirements of futuristic smart cities, the authors introduce the emph{Salient-Classifier}, a novel architecture combining a kernelized approach with a residual learning strategy. We evaluate variations of SSIVD-Net and Salient Classifier on our SCVD dataset and benchmark against state-of-the-art (SOTA) models commonly employed in violence detection. Our approach exhibits significant improvements in detecting both weaponized and non-weaponized violence instances. By advancing the SOTA in violence detection, our work offers a practical and scalable solution suitable for real-world applications. The proposed methodology not only addresses the challenges of violence detection in CCTV footage but also contributes to the understanding of weapon distribution in smart surveillance. Ultimately, our research findings should enable smarter and more secure cities, as well as enhance public safety measures.</p>" @default.
- W4385800510 created "2023-08-15" @default.
- W4385800510 creator A5007460191 @default.
- W4385800510 date "2023-08-14" @default.
- W4385800510 modified "2023-09-27" @default.
- W4385800510 title "SSIVD-Net: A Novel Salient Super Image Classification & Detection Technique for Weaponized Violence" @default.
- W4385800510 doi "https://doi.org/10.36227/techrxiv.23898039.v1" @default.
- W4385800510 hasPublicationYear "2023" @default.
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