  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 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.
