Agent-based computer vision in a dynamic, real-time environment

Abstract For computer vision systems to operate in many real-world environments, processing must occur in real-time under dynamic conditions. An agent-based methodology offers an approach to increase flexibility and scalability to accommodate the demands of a real-time, dynamic environment. This paper presents an agent-based architecture that uses a utility optimization technique to guarantee that important vision tasks are fulfilled even under resource constraints. To ensure that the processing of vision tasks is both reliable and flexible, multiple behaviors are utilized to accomplish the vision application's requirements. A vision behavior consists of a grouping of vision algorithms and a set of service levels associated with these algorithms. Utility functions are adopted to evaluate the performance of all possible behaviors that can address the requirements of a vision application within resource constraints. The maximum overall utility corresponds to the optimal behavior. Two example systems using this model are presented to show the applicability of the architecture. Experimental results show that this agent-based architecture outperforms traditional non-agent-based approaches.