FILM: FineGrained Book Localization with Mobile RFID Scanning Misshelving is an ageold problem in a library. If this happens, users can hardly find their wanted books, leading to a waste of book resources. In this paper, we investigate the problem of book localization through mobile RFID scanning. According to extensive experiments, we find that the phase data (that are used to do localization) beyond the reader antennau0027s main lobe are noisy; taking these data as the input of book localization is likely to lower the localization accuracy. However, it is not easy to figure out which data are noisy. To address this problem, we propose a finegrained localization method called FILM, which can achieve cmlevel book localization through the following three steps. First, it collects the phase profile for each tagged book as is. Second, it sophisticatedly chooses three samples from the phase profile and locates the tagged book by modeling the three samples. Since there are a great number of 3combinations, we can get many candidates of the book localization. Third, it uses the Gaussian kernel density model to determine whether the candidates are good or not. The basic idea is that, the noisy data give rise to uncertain estimates, whereas the clean data will always get an estimate close to the ground truth. Therefore, the position with the highest density is the most likely to be the tagu0027s position. By this means, the noisy data do not contribute to the final estimate, which improves the localization accuracy. We setup a prototype of FILM and experiment results show that FILM is superior to the stateoftheart, with a small localization error of 1.3cm.

Fast Method of ID Documents Location and Type Identification for Mobile and Server Application In this paper we discuss the problem of simultaneous document type recognition and projective distortion parameters estimation for the images of ID documents. There are two considered cases. In the first case a video stream captured using mobile devices is processed on the device. The second case considers photos or scanned images which are processed on a server. For each case the requirements are defined for the input data and processing speed. The universal approach is proposed, which allows solving the problem in both cases. The approach is based on representing the image as a constellation of feature points and descriptors, but in order to perform more accurate distortion parameters estimation straight lines and quadrangles are extracted from the input image and used as additional features. Techniques are described which allow to combine matched feature points, lines, and quadrangles to geometric verification using RANSAC. Best alternative selection criteria are proposed along with methods of solution accuracy estimation. The differences between methods of preliminary analysis of the input image and geometric primitives location are discussed in relation to the considered problems. For quality estimation an open dataset MIDV500 is used, together with its extension for serverside problem version, created in scope of this work. Results show that using lines and quadrangles increase the location accuracy, and the proposed algorithm surpasses previously published works in classification precision and computational performance.