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Detection of violence and weaponized violence in closed-circuit television(CCTV) footage requires a comprehensive approach. In this work, we introducethe \emph{Smart-City CCTV Violence Detection (SCVD)} dataset, specificallydesigned to facilitate the learning of weapon distribution in surveillancevideos. To tackle the complexities of analyzing 3D surveillance video forviolence 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 datacomplexity, dimensionality, and information loss while improving inference,performance, and explainability through salient-super-Image representations.Considering the scalability and sustainability requirements of futuristic smartcities, the authors introduce the \emph{Salient-Classifier}, a novelarchitecture combining a kernelized approach with a residual learning strategy.We evaluate variations of SSIVD-Net and Salient Classifier on our SCVD datasetand benchmark against state-of-the-art (SOTA) models commonly employed inviolence detection. Our approach exhibits significant improvements in detectingboth weaponized and non-weaponized violence instances. By advancing the SOTA inviolence detection, our work offers a practical and scalable solution suitablefor real-world applications. The proposed methodology not only addresses thechallenges of violence detection in CCTV footage but also contributes to theunderstanding of weapon distribution in smart surveillance. Ultimately, ourresearch findings should enable smarter and more secure cities, as well asenhance public safety measures.

SSIVD-Net: A Novel Salient Super Image Classification & Detection Technique for Weaponized Violence