Location Estimation of an Urban Scene using Computer Vision Techniques

The process of adding the geographical identification data to an image is called geotagging and is important for a range of applications starting from tourism to law enforcement agencies. The most convenient way of adding location metadata to an image is GPS geotagging. This article presents an alternative way of adding the approximate location metadata to an urban scene image by finding similar images in a dataset of geotagged images. The matching is done by extracting the image features and descriptors and matching them. The dataset consists in geotagged 360 degrees panoramic images. We explored three methods of matching the images, each one being an iteration of the previous method. The first method used only feature detection and matching using AKAZE and FLANN, the second method performed image segmentation to provide a mask for extracting features and descriptors only from buildings and the third method preprocessed the dataset to obtain better accuracy. We managed to improve the accuracy of the system by 25%. Following the in-depth analysis of the results we will present the results as well as future improvements.