How Gen AI and Cisco Can Turbo-Charge Video Surveillance To Boost Operational Efficiency

7 min read

Many businesses increasingly rely on video surveillance capabilities to provide efficient, real-time insights often from remote locations, but struggle to translate these copious quantities of data into effective decision-making.

However, Generative AI technologies (Gen AI) can now enable automated analysis of video streams, allowing for simultaneous characterization and understanding of frames to perform tasks such as object detection, classification, and anomaly detection. This significantly enhances operational efficiency, risk management, and compliance in industries that rely heavily on this surveillance data.

How Gen AI and Cisco Can Turbo-Charge Video Surveillance To Boost Operational Efficiency

At a time when data-driven decision-making has become even more critical, industries from financial services to energy are now discovering the transformative potential of AI solutions to make sense of these images and drive value from them. In addition, the complexity of regulatory environments and the rising costs of compliance underscore the need for intelligent systems that can quickly analyze and interpret visual information.

Proactive strategies

Gen AI not only streamlines processes but also empowers organizations to harness their data more effectively, providing them with a competitive edge in an increasingly digital landscape. By adopting Gen AI-driven monitoring systems, businesses can move towards proactive and informed strategies, redefining how they manage safety, compliance, and operational integrity.

In a detailed white paper we outline how SoftServe deployed a computer vision and retrieval-augmented generation (RAG) application within a Cisco-provided lab environment to meet these challenges. By leveraging Cisco’s advanced hardware capabilities, we demonstrate how on-premises solutions can effectively support the demands of Gen AI applications.

Cisco collaboration

How Gen AI and Cisco Can Turbo-Charge Video Surveillance Image

Our collaboration with Cisco, who has named SoftServe as its Strategic go-to-market partner for Gen AI activity, underscores a shared vision of harnessing technology to solve complex problems, enabling organizations to integrate AI seamlessly into their operations. The deployment highlights not only our technical expertise but also the transformative potential of AI in driving innovation across various industries.

In this initiative, we focus on building an infrastructure that enhances the performance and scalability of Gen AI applications. By utilizing Cisco's UCS C240-M6 servers equipped with NVIDIA GPUs, we optimized computational resources for high-demand workloads. The architecture is designed to ensure smooth data processing and rapid response times, essential for applications that rely on real-time analytics and insights. Through this deployment, we found how effective resource allocation and strategic design choices contribute to overall system efficiency.

Monitoring and observability are critical components of our deployment strategy. As organizations embrace AI-driven solutions, maintaining visibility into system performance and resource utilization is crucial for informed decision-making. Our approach integrates comprehensive monitoring tools that provide actionable insights, enabling proactive management of the environment. This ensures that any anomalies or performance bottlenecks are addressed promptly, supporting an uninterrupted operation of the AI applications.

Meet evolving needs

The paper delves into the specifics of our implementation, detailing the architectural framework, deployment process, and monitoring strategies employed. By sharing our experiences and findings, we provide valuable insights into the successful deployment of Gen AI applications on-premises. We also showcase the potential of Cisco hardware in facilitating innovative solutions that meet the evolving needs of modern enterprises.

Our application design brings together advanced technologies to deliver a user experience that's both seamless and efficient, including automating the process of video captioning, which we then embed into our vector database. This two-step process enables a RAG design that enhances historical search capabilities. It means users can effortlessly explore events within specific time ranges in the past, with the system efficiently retrieving and presenting the most relevant information.

Digital security

In an age where data privacy and security are more critical than ever, our application doesn't just adapt — it leads the charge into the future of secure digital interactions. Understanding the profound sensitivity of historical data and the sophisticated risks associated with machine learning, we've engineered a comprehensive suite of cutting-edge protective measures that set new industry standards.

By integrating advanced security measures, our application emerges as a resilient platform ready to navigate the complexities of tomorrow's digital landscape. We're not just keeping up with trends, we're defining them. This ensures a safe, innovative, and responsible environment where users can confidently explore historical data and leverage advanced functionalities, today and into the future.

Cost efficiencies

Building the solution on open-source multimodal Gen AI models offers significant flexibility and cost savings. The self-hosted deployment is estimated to be five times cheaper than cloud-based alternatives due to the high running costs associated with multimodal inputs in cloud environments. This cost efficiency enhances scalability and accessibility for extensive implementations.

However, challenges arise when scaling the system to manage thousands of cameras and large volumes of video footage, including issues with data retention, storage capacity, and processing power. Fine-tuning limitations of the model can impede customization and optimization for specific use cases, affecting overall performance. To address these challenges, implementing distributed computing resources and efficient data management strategies is essential, including key elements such as:

Scalable Storage Solutions: Employing cloud-native storage systems or on-premises scalable storage architectures that can handle large datasets efficiently.

Data Compression and Retention Policies: Utilizing video compression algorithms and setting intelligent data retention schedules to reduce storage requirements without losing critical information.

Conclusion

The development of a chatbot that enables users to interact with real-time and historical CCTV camera footage is a significant advancement for security management. This application not only transforms traditional surveillance systems into interactive tools but also enhances situational awareness and decision-making capabilities.

By allowing users to query and analyze historical footage through natural language, organizations can extract valuable insights and respond proactively to incidents, thereby improving safety and operational efficiency. As the demand for advanced surveillance technology grows, this innovative approach addresses the evolving needs of businesses and communities alike.

The infrastructure supporting this application, backed by Cisco hardware, plays a crucial role in ensuring its success. Cisco's advanced technology provides a reliable framework that facilitates seamless communication between components, allowing the system to scale effectively and manage the complexities of real-time data processing and storage.

This integration of hardware and software not only optimizes application performance but also positions the solution as an essential asset for the future of security management. By leveraging AI and machine learning within a robust infrastructure, organizations can redefine their engagement with surveillance systems, ultimately fostering a safer and more responsive environment.

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