Floor Fingerprint Verification Using a Gravity-Aware Smartphone

Abstract

This paper presents our research on location estimation based on the identity of floor surface patterns, which we call "floor fingerprints," from a photographic image of a floor taken with a hand-held smartphone. Because floor textures generally appear to lack sufficient features, it may seem difficult using general feature detection algorithms to find matching pairs of features in two corresponding floor images taken at an identical location but from different orientations of the camera and under different lighting conditions. We demonstrate, however, that use of a preprocessing image filter, involving gravity-rectified image adjustment against perspective distortion and enhancement of local image features, provides well-aligned detail sufficient to allow detection of paired features of floor textures. Although the enhancement filter reveals many noisy pairs in local feature detection, we show that it is possible to choose a valid imageto-image correspondence efficiently using our newly proposed B-ORB feature detector and RANSAC. Since matching a query floor image with large-scale floor images stored at the server requires a large amount of processing resources, we utilize GPGPU for the feature detection and matching. This paper proves the feasibility and efficiency of the proposed approach, based on our experimental results concerning the accuracy and processing time, and discusses possible solutions to a wide range of real-world indoor location applications.

Data- and Algorithm-Hybrid Approach for Imbalanced Data Problems in Deep Neural Network

“…We then conducted a performance evaluation using a texture data set, to see if our approach is applicable to floor texture image classification and identification problem [14] [15]. Texture data from CUReT (grey) [16] set consists of 61 classes with 92 samples for each class including test set.…”