Investigation of a scene recognition method for indoor use focusing on information around static objects.

Abstract

In recent years, there has been a lot of research on scene recognition, which estimates the situation from a video image. There are two main methods for scene recognition, one using BoVW (Bag of Visual Words) and the other using CNN(Convolutional Neural Network). Both methods use local and visual feature vectors in images to achieve scene recognition. On the other hand, humans recognize scenes and understand the situation by considering the objects they see, their numbers, and the relationships between them. Thus, performing recognition that focuses on the objects in the target image, it is possible to perform scene recognition that semantically understands the situation like humans do. Therefore, we propose an indoor scene recognition method that classifies object information in images obtained by object detection into “static objects ”and “dynamic objects ”and uses the information on the frequency of “dynamic objects ”around “static objects ”as feature vectors.