Object Recognition Architecture Using Distributed and Parallel Computing with Collaborator

Abstract

These days, object recognition is regarded as a sufficient condition for essential requirements of intelligent service robot. Under such demands, object recognition's algorithms and its methods have been increasing in complexity along with the increase of computational ability. Despite these developments, object recognition still consumes many computational resources, which impede total time throughput drop. The purpose of this paper is to suggest an object recognition software architecture, which reduces time throughput by applying concepts of 'Component based approach' and COMET (Concurrent Object Modeling and architectural design mEThod), a computational efficiency improvement method. In COMET, the component based approach reduces total time throughput by supporting dynamic distributed and parallel processing. To enable these computations, surplus computational resources of nearby collaborator robot can be used for distributed computing by SHAGE, which is a component management framework based on COMET. Using SHAGE, in order to connect physical operation among components, software function module should be a componentized component defined by 'COMET component design guideline'. This paper componentizes the object recognition software function modules via this guideline, and shows the object recognition architecture as a connected relationship among these components. The experimental results show a maximum of 42% performance improvement compared to the original multi-feature evidence recognition framework.