Towards a Scalable Server-Side Architecture for Real-Time Distributed Image Analysis

• Nivardo A. L. Castro IFCE
• Cidcley T. Souza IFCE
• A. Wendell O. Rodrigues IFCE
• Carlos H. L. Cavalcante IFCE

Resumo

The growth of camera networks in various areas has increased the demand for scalable intelligent real-time image processing solutions in surveillance and analytics. Python, known for its extensive AI and machine learning ecosystem, is a popular choice for image analysis tasks, but its limitations, such as the Global Interpreter Lock (GIL), pose challenges for efficient parallel processing. This paper presents a distributed architecture that uses message-oriented middleware (MOM) to orchestrate scalable, asynchronous data pipelines and enable Python-based image analysis. Using standard protocols such as RTSP, the architecture ensures compatibility with existing camera systems and supports seamless integration. Performance is evaluated using M/M/c queuing theory metrics, showing significant scalability improvements and near-linear throughput gains. The proposed architecture demonstrates not only scalability improvements but also applicability across a range of high-throughput scenarios.

Palavras-chave: Visualization, Image analysis, Scalability, Surveillance, Computer architecture, Cameras, Throughput, Systems engineering and theory, Real-time systems, Standards, Distributed systems, real-time image analysis, high-demand visual data processing