Position Mapping using Content Based Image Retrieval and Annoy

Geolocation refers to the identification of the geographic location of a user or computing device via a variety of data collection mechanisms. The proposed system aims to enable accurate and efficient localization of objects within a given environment. Traditional positioning systems often rely on GPS or other sensor-based technologies, which may face limitations in indoor or highly cluttered environments. To overcome these challenges, our approach leverages the power of CBIR, which involves matching query images with a pre-existing database of reference images. By extracting key features from images and employing Annoy, a fast approximate nearest neighbor library, we achieve efficient retrieval and comparison of images. The extracted features act as robust descriptors, enabling accurate positioning of objects based on visual similarities. This research contributes to the advancement of positioning systems by introducing a novel methodology that combines CBIR, Annoy, and feature extraction techniques, offering a promising solution for accurate object localization in challenging environments.The dataset that has been is used is gathered by the authors and the dataset plays a crucial role in position mapping, enabling accurate and context-aware positioning within a given environment.The results demonstrate the effectiveness of the proposed position system, as it achieves high accuracy and real-time performance. This research contributes to the advancement of positioning systems by introducing a novel methodology that combines CBIR, Annoy, and feature extraction techniques, offering a promising solution for accurate object localization in challenging environments.