# An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC
## 1 Introduction  
The prospect of a social robot in every home may be realized within the next two decades. There are already many researchers in academia and tech industries that are actively studying and designing prototypes of such robots. The open research objectives are diverse and include, but are not limited to, emotion recognition, perception, pattern recognition (face, object, scene, and voice), and navigation. These robots are expected to be employed as companions to seniors and children, housekeeping, surveillance, etc. [1, 2]. In order to accomplish such tasks, it is essential that the robot seamlessly recognizes its own location inside the home--similar to humans who are effortlessly aware of their whereabouts at any instant, e.g., kitchen or living room. This knowledge is a pretext for many indoor navigation scenarios, and facilitates the robot's movement in the house. This paper is not about designing social robots per se; it addresses one of the many problems that collectively contribute towards efficient operation of such robots; namely knowing its location in the house at any given instant. Classification is a core computer vision problem whereby data streams are categorized into specific classes in accordance to learning their specific features. The problem has been addressed by different supervised machine learning algorithms [3]. The convolutional neural network (CNN) [4, 5] is generally regarded as the state-of-the-art algorithm in deep learning for visual purposes, e.g., face recognition and object detection, especially after the pioneering work reported in Reference [6]. Thisalgorithm surpasses conventional machine learning algorithms by integrating the feature extraction and classification problems without the requirement of careful human design [7].  
The main objective of this paper is to identify different household rooms for social robotic applications in houses. The study is part of a larger project of designing social robots to be employed in such environments. The problem of indoor navigation can be addressed by different approaches; here, we propose a CNN solution for real time implementation for such social robots. We examined several CNN architectures within a home setting. The latest scene dataset, called Places [8], was adopted in the study. We downloaded the most common five indoor classes for houses (bedrooms, dining rooms, kitchens, living room, and bathrooms) from the dataset. However, we noted that each class included a sizeable number of irrelevant scenes; we therefore reduced the number of samples by removing unrelated images from each class. We then propose a combination solution of CNN with multi-binary classifiers, referred to as ECOC [9]. These models were evaluated experimentally on a NAO humanoid robot [10].  
The rest of the paper is organized as follows: Section 2 discusses the related reported literature that addresses the room classification problem for robotics employing different methods. In Section 3, we briefly review CNN and ECOC algorithms. We begin with fundamental components of CNN that are used in most CNN architectures. This section also explains the main idea of ECOC approach. Section 4 focuses on all simulation experiments and examines the results. We start with a brief overview of the scene dataset. We then present simulation studies of multi-class experiments for several CNN architectures as well as results of multi-binary classifiers on the best CNN architecture. Section 5 shows the results of real-time experiments on all three models tested on a NAO humanoid robot. The paper is concluded with a discussion of the results in Section 6.