Toward a Scalable Video Surveillance System Using Containerization and Object Storage Technologies

Designing a scalable Video Surveillance System (VSS) with integrated analytics presents significant challenges. Real-time processing of massive video streams imposes heavy demands on bandwidth and computing resources, making distributed architectures such as microservices and containerization difficult to optimize for low latency. High-resolution video requires petabyte-scale storage, necessitating efficient compression, tiered storage (hot/warm/cold), retention policies, and robust indexing of metadata and analytic outputs (e.g., motion events, object types). Real-time analytics, including object detection, facial recognition, and behavior analysis, demand advanced deep learning models and lifecycle management frameworks to balance accuracy with processing delay. This paper introduces a design framework for scalable VSS that integrates edge-cloud computing, meta-indexed object storage, and containerized architecture to address these challenges.