Visual Place Recognition Using Region of Interest Extraction with Deep Learning Based Approach

Abstract

AbstractVisual Place Recognition (VPR) is process of properly identifying a formerly visited place under varying viewpoints conditions. VPR becomes a challenging task due to the variations in lighting conditions (day time or night time), shadows, weather conditions, view points, or even different seasons. Therefore, it is needed to develop VPR algorithms which can handle the variation in visual appearances. This article designs a visual place recognition using a novel region of interest (RoI) extraction with deep learning based (VPR-ROIDL) technique on changing environments. The proposed VPR-ROIDL technique initially enables the extraction of RoIs using saliency map then features of RoIs using local diagonal extrema patterns (LDEP) respectively. Besides, the extracted features of RoIs are passed into the convolutional neural network based residual network (ResNet) model and the computed deep features are stored in the database. Upon providing a query image (QI), the RoIs and features of RoIs are extracted as same as reference images. Moreover, the deep features from the ResNet model are generated and Euclidean distance based similarity measurement is used to finally recognize the places. By the use of RoI and descriptors matching process, the VPR-ROIDL model has the ability to recognize places irrespective of changing illuminations, seasons, and viewpoints. The simulation results pointed out the supremacy of the ROIDL-VPR technique over the recent state of art approaches.KeywordsVisual Place recognitionDeep learningVisual appearancesRegion of InterestSaliency mapSimilarity Measurement