Indoor object recognition using pre-trained convolutional neural network

Abstract

Indoor object recognition is a key task for mobile robot indoor navigation. In this paper, we proposed a pipeline for indoor object detection based on convolutional neural network (CNN). With the proposed method, we first pre-train an off-line CNN model by using both public Indoor Dataset and private frames of videos (FoV) dataset. This is then followed by a selective search process to extract a region of interest (RoI) after the input video was parsed into frame images. The extracted RoIs are then classified into candidates using the pre-trained deep model and the candidates between the nearest frame images are refined using detection fusion. Finally, the annotated frames are merged to create video as the output. The experiments show that our design is very efficient against indoor object detection.