Meruna developed a threaded camera data pipeline for the una-auxme/arlab repository, focusing on continuous frame capture and publishing to support real-time computer vision analytics. Using Python and leveraging ROS2 within an embedded systems context, Meruna decoupled the image capture and publish workflows by refactoring I/O operations into a dedicated thread. This approach reduced frame drops and improved reliability by introducing robust error handling for missing frames and ensuring safe camera shutdown procedures. The work enhanced maintainability and established a foundation for future performance optimizations, demonstrating a solid understanding of concurrent programming and system reliability in embedded computer vision applications.