# Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
## 1 Introduction  
The proliferation of modern smartphones and their wide usage in the everyday life makes them a perfect tool for localization, especially indoors. The high definition camera-equipped phones carry other sensors as well which can be employed to locate a person. Studies [1; 2; 3] indicate that the images from a smartphone camera can be used for very accurate scene recognition both outdoor, as well as, indoor. We aim to use deep convolutional neural networks (CNNs) to identify the specific scenes in the indoor environment to assist the accurate location estimation. Radio signal strength fingerprinting based indoor localization has been widely utilized as it makes the use of already deployed access points (AP). However, its performance is severely degraded due to the dynamic factors of shadowing and multipath effects. On the contrary, geomagnetic field (referred to as \"magnetic field\" in the rest of the paper) based indoor localization has emerged as a potential technology which can overcome the issues related to radio signal based localization. At the same time, it is pervasive, less prone to changes in indoor infrastructure and does not require additional sensors to perform localization. However, infrastructural changes involving ferromagnetic materials like iron, nickle, etc., make changes to the magnetic field. For example, installing an elevator in a building would cause a huge magnetic change in the proximity of the elevator [4]. Many works [5; 6; 7; 8; 9; 10] focusing on the use of the magnetic data have been presented. However, they are limited by two factors. First, they work in single floor environments which limits their wide applicability. Second, they do not meet the accuracy requirements for emergency response actions as either the localization errors are high in these works or they make the use of radio signals. The use of radio signals makes them vulnerable, whereas they need the deployed APs to work properly. This research aims to overcome the limitations of the traditional magnetic field based localization and devises an approach which works in a multi-floor environment. The images from the smartphone camera are used to identify indoor scenes. Scene recognition is one of the challenging but important tasks in computer vision which has many applications including object detection [11], place identification [12], localization [13; 14], robotics [15], etc. Scene recognition also plays an important role in objects tracking [16; 17; 18], computer engineering tasks including the modeling of resident recognition and monitoring in modern smart home applications [19; 20]. Similarly, research [21] has shown that scene recognition can significantly improve the health care services by monitoring the patients. Security applications have also been improved by utilizing scene recognition [22]. Scene recognition is broadly divided into two categories: Outdoor scene recognition and indoor scene recognition. Research works on outdoor scene recognition involve camera pose estimation [23], semantic scene classification [24], motion detection [25], satellite image based tasks [26], localization [27], etc. A variety of sensors have been utilized for indoor scene recognition based tasks. For example [28; 29], used RBG-D camera for object detection and human pose estimation. Similarly research works [17; 30; 31] have utilized depth images for human activity recognition and monitoring the individuals in smart homes. The use of digital camera to extract image features for classification and segmentation has also been done in [32]. Scene recognition involves the use of both low-level features including color, Local Binary Pattern (LBP), Scale-invariant Feature Transform (SIFT), etc., as well as high level features like semantic modeling including bag-of-visual-features, and attributes and object-based approaches. However, such approaches require a large amount of manned effort to label the captured images. Recently, deep CNN have been utilized for a variety of computer vision problems.