Office-mate: Selective attention and incremental object perception
Minho Lee, Young-Min Jnag, Sang-Woo Ban · 2009
We propose an autonomous robot vision system that is applied to develop an intelligent artificial officemate. In order to operate the proposed system in real environment, it is very important for the officemate to be able to adapt to an environmental changes that may occur in an indoor environment. Novelty detection is one of essential functions for the officemate to detect a situation change. The proposed system can indicate a novel scene and a scene change based on a visual selective attention module. Moreover, it can adaptively acquire new information based on incremental object perception, face recognition, and emotion representation. In order to implement an on-line officemate system, we implement an efficient model by simplification and optimization procedure which can reduce the computation load. Experimental results show that the developed system successfully identifies a change of natural scenes and incrementally learns an arbitral object and a face, and it can also extend its knowledge through interaction with human supervisor.