Combining RGB and Depth Images for Indoor Scene Classification Using Deep Learning

摘要

There has been significant progress in recognition of outdoor scenes but indoor scene recognition is still an challenge. This is due to the high appearance fluctuation of indoor situations. With the recent developments in indoor and mobile robotics, identifying the indoor scenes has gained importance. Many approaches have been proposed to detect scenes using object detection and geotags. In contrast, the proposal of this paper uses the convolutional neural network which has gained importance with advancement in machine learning methodologies. Our method has higher efficiency than the existing models as we try to classify the environment as a whole rather than using object identification for the same. We test this approach on our dataset which consists of RGB and also depth images of common locations present in academic environments such as class rooms, labs etc. The proposed approach performs better than previous ones with accuracy up to 98%.