A Scalable Distributed Architecture for Intelligent Vision System

The complexity of intelligent computer vision systems demands novel system architectures that are capable of integrating various computer vision algorithms into a working system with high scalability. The real-time applications of human-centered computing are based on multiple cameras in current systems, which require a transparent distributed architecture. This paper presents an application-oriented service share model for the generalization of vision processing. Based on the model, a vision system architecture is presented that can readily integrate computer vision processing and make application modules share services and exchange messages transparently. The architecture provides a standard interface for loading various modules and a mechanism for modules to acquire inputs and publish processing results that can be used as inputs by others. Using this architecture, a system can load specific applications without considering the common low-layer data processing. We have implemented a prototype vision system based on the proposed architecture. The latency performance and 3-D track function were tested with the prototype system. The architecture is scalable and open, so it will be useful for supporting the development of an intelligent vision system, as well as a distributed sensor system.