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Automatic violence detection in video has become a critical need for public and corporate security, especially given the exponential growth of surveillance cameras. Traditional approaches based on CNNs and Transformers, while effective at extracting spatiotemporal features, have limitations in handling long-term dependencies and computational efficiency. In this context, FuseMamba-VD emerges, an architecture that combines a dual-branch design with a state space model (SSM) as backbone. One branch specializes in spatial features while the other captures temporal dynamics; both are fused via a gating mechanism (GCTF) that improves detection even in complex scenarios.
This approach not only achieves state-of-the-art performance on benchmarks like DVD or the new unified dataset combining RWF-2000, RLVS, SURV, and VioPeru, but also offers an optimal balance between accuracy and computational cost. For companies looking to implement intelligent video surveillance solutions, technologies like FuseMamba-VD demonstrate the potential of SSM models for scalable and efficient applications. At Q2BSTUDIO, we understand that each organization has specific needs, which is why we offer artificial intelligence for businesses that enables the integration of customized video analysis systems, whether through AI agents or advanced detection modules.
Adopting these solutions requires a robust and secure infrastructure. Therefore, we combine custom applications with AWS and Azure cloud services to ensure scalability, and cybersecurity services that protect both data and deployed models. Additionally, the metrics generated by detection systems can be integrated with business intelligence tools like Power BI, enabling decision-makers to make data-driven decisions. From custom software design to AI implementation for businesses, at Q2BSTUDIO we accompany our clients through every phase of the technology cycle, including AI agents and process automation, to transform security into a real competitive advantage.
