Query: floor recognition

title: Binary Patterns Based Floor Recognition Suitable for Urban Scenes
abstract: Nowadays urban structures (lines, planes, spheres, etc. recognition is a useful task under computer vision systems since it provides rich scene information that can be exploited to understand the scene. In this context, one popular trend is for urban planar structures recognition (floor/ground recognition) because they have consistent appearance under urban scenarios and they can be used to improve several computer vision applications performance, for example, in autonomous vehicle navigation, robotic control, 3D modeling, etc. In the current literature, there are several approaches for floor recognition. However, most previous work has low robustness under image degradations (blur, lighting changes, noise, etc. One alternative to address the image degradation problems is the use of binary features (for example LBP features) In this work, we propose a new binary patterns based floor recognition suitable for urban scenes. For that, we propose two analyses, first we consider the floor connection to increase the recognition and second we segment the recognition in floor surface sets to remove the floor misrecognition. Finally, experimental results demonstrated that the proposed method delivers high stability under different scenes and it has more recognition than previous work under floor recognition domain.

title: A Binary Descriptor Invariant to Rotation and Robust to Noise (BIRRN) for Floor Recognition
abstract: Floor recognition is a conventional task in computer vision with several applications in different fields, from augmented reality to autonomous driving. To address this problem, there is a plethora of methods, several of them based on the use of visual descriptors. However, most previous work has low robustness under image degradation. One alternative to address image degradation problems is the use of binary descriptors. Unfortunately, these descriptors are sensitive to noise. In addition, these descriptors use only some pixels within a patch, this limits the floor recognition scope since useful information is available just for a small pixel set. To cope with these problems, we propose a new texture descriptor based on binary patterns suitable for floor recognition. This descriptor is robust to noise, robust to illumination changes, invariant to rotation and it considers a larger number of pixels than the used in the previous LBP based approaches. Experimental results are encouraging, the proposed texture descriptor reach high performance under several real world scenarios, 7.4% more recall and 3.7% (F score\ than previous texture descriptors and it has high robustness under image degradation.

title: Floor Recognition Based on SVM for WiFi Indoor Positioning
abstract: With the rapid development and popularization of WiFi technology, indoor positioning technology based on WiFi has become a research hot spot. At present, a variety of complex structures come into being along with the gradual improvement of people's living standards. The two dimensional positioning of the room has been unable to meet people's needs. In this paper, a method of WiFi indoor positioning for floor recognition based on SVM (support vector machine) classification is proposed. Due to the obvious change during the WiFi signal through the wall or floor, floor identification is realized quickly and accurately by using SVM. We collect the RSS data of each floor with smart phone, and utilize the 10 fold cross validation method to train classifier model and evaluate classification accuracy. The experimental results show that the high accuracy of floor discrimination, and the 99.09% floor recognition accuracy can be obtained.

title: Hybrid approach for reliable floor recognition method
abstract: Location based services and context aware services of smartphones have been paid much attentions. For these services, location information is important. Therefore, several positioning methods have been developed. This research focuses on unnecessary operation of GPS in seamless Indoor/Outdoor positioning. In order to reduce unnecessary behavior of GPS, we propose two floor recognition methods. First method is an advanced floor recognition method by using Wi Fi and barometer in smartphones, when we know the location of Wi Fi APs and the height between floors of buildings. Second method is to learn and infer if a floor of a building whose location of APs is unknown is connected to outside of the building. We conducted an evaluation of the proposed methods. As a result, the accuracy of the first method was 96% The evaluation result also shows that the second method can learn and infer the floor which is connected to outside of the building.

title: Sea floor recognition with bio inspired echolocation
abstract: This paper presents a study of different feature extraction methods for sea floor recognition. This analysis is based on the frequency components of the return signals from a bio inspired wideband sonar. Part of the sea floor of Tallinn Bay was surveyed using a wideband sonar with linear chirp pulses. The area contains citadel constructions and sediment regions. The recognition task is to distinguish between these two types of the sea floor. It was observed that the frequency spectrum of the signals returned from these two surfaces has different distributions. The work presents three frequency based approaches for the feature extraction process: Time Frequency Moment Singular Value Decomposition (tFM SVD) Energy Vector and Complete Frequency Spectrum. Comparison of the techniques is made in terms of the classification results.

title: A Practical Floor Localization Algorithm Based on Multifeature Motion Mode Recognition Utilizing FM Radio Signals and Inertial Sensors
abstract: Floor localization plays a crucial role in multi storey indoor positioning systems. Wi Fi enabled system and barometer based system are two well known systems in floor localization. However, a practical floor localization system should consider the absence of infrastructure, the pretrained database and the influence of complex environment. Inertial sensors based systems realize floor localization by classifying pedestrian's motion states, which can avoid the above problems. Nevertheless, the accuracy is limited because of the stochastic noise introduced by device measurement. Besides, only single acceleration feature is utilized in most of the existing systems. Fusion of multiple inertial sensors and other signals such as frequency modulated (FM) radio signals is a feasible solution considering both practicality and accuracy. In this paper, a multi feature floor localization algorithm using MEMS inertial sensors and a FM radio receiver is designed. Firstly, based on the MEMS inertial sensors and FM radio signals, we select features which are not affected by types or sites of stairs for the classification of plane walking, going upstairs, and going downstairs moments. Secondly, random forest classifiers are introduced to obtain accurate classification results by combining the above features. Floor is located on the basis of classification results. Field experiments have been conducted at four sites in two office buildings to evaluate the performance of the proposed algorithm. The results demonstrate that the multi feature approach achieves 96.9% floor localization accuracy, which is 38.4% higher than the method using only single acceleration feature and 24.3% higher than the multi feature algorithm without FM features.

title: Problematic Pixels: Prehistoric Residential Floor Recognition in the Pend Oreille Valley

title: Deep Floor Plan Recognition Using a Multi Task Network With Room Boundary Guided Attention
abstract: This paper presents a new approach to recognize elements in floor plan layouts. Besides walls and rooms, we aim to recognize diverse floor plan elements, such as doors, windows and different types of rooms, in the floor layouts. To this end, we model a hierarchy of floor plan elements and design a deep multi task neural network with two tasks: one to learn to predict room boundary elements, and the other to predict rooms with types. More importantly, we formulate the room boundary guided attention mechanism in our spatial contextual module to carefully take room boundary features into account to enhance the room type predictions. Furthermore, we design a cross and within task weighted loss to balance the multi label tasks and prepare two new datasets for floor plan recognition. Experimental results demonstrate the superiority and effectiveness of our network over the state of the art methods.

title: Real time floor recognition in indoor environments using TOF Camera

title: Text Recognition and Classification in Floor Plan Images
abstract: In this paper we present a method for text recognition in floor plan images. In particular, we are concerned about locating, reading, and categorizing text inside floor plan images to obtain information about the building. Furthermore, the aim of this paper is to compare traditional text detection methods, based on image processing techniques, with recent approaches relying on convolutional neural networks. To improve results we combined several methods outperforming the original ones. Text regions are also classified in four semantic classes according to their purpose. Two datasets with different features, including quality and size, were considered in the experiments performed.