Scene recognition based on DNN and game theory with its applications in human-robot interaction

TL;DR

This paper introduces a novel scene recognition approach combining deep neural networks and game theory, applied to enhance human-robot interaction through improved scene understanding and robot control.

Contribution

It proposes a new DNN model integrated with game theory for scene recognition, transforming image registration into a Markov Random Field energy minimization problem.

Findings

Effective scene recognition performance demonstrated

Enhanced robot vision for multiple tasks validated

Innovative combination of DNN and game theory

Abstract

Scene recognition model based on the DNN and game theory with its applications in human-robot interaction is proposed in this paper. The use of deep learning methods in the field of scene recognition is still in its infancy, but has become an important trend in the future. As the innovative idea of the paper, we propose the following novelties. (1) In this paper, the image registration problem is transformed into a problem of minimum energy in Markov Random Field to finalize the image pre-processing task. Game theory is used to find the optimal. (2) We select neighboring homogeneous sample features and the neighboring heterogeneous sample features for the extracted sample features to build a triple and modify the traditional neural network to propose the novel DNN for scene understanding. (3) The robot control is well combined to guide the robot vision for multiple tasks. The experiment is then conducted to validate the overall performance.