A Layered Active Memory Architecture for Cognitive Vision Systems

Abstract

Recognising actions and objects from video material has attracted growing research attention and given rise to important applications. However, injecting cognitive capabilities into computer vision systems requires an architecture more elaborate than the traditional signal processing paradigm for information processing. Inspired by biological cognitive systems, we present a memory architecture enabling cognitive processes (such as selecting the processes required for scene understanding, layered storage of data for context discovery, and forgetting redundant data) to take place within a computer vision system. This architecture has been tested by automatically inferring the score of a tennis match, and experimental results show a significant improvement in the overall vision system performance --- demonstrating that managing visual data in a manner more akin to that of the human brain is a key factor in improving the efficiency of computer vision systems.