Related papers: GAMa: Cross-view Video Geo-localization

GAMa: Cross-view Video Geo-localization

URL: http://arxiv.org/abs/2207.02431v1
Date: Wed, 6 Jul 2022 04:25:51 GMT
Title: GAMa: Cross-view Video Geo-localization
Authors: Shruti Vyas, Chen Chen, and Mubarak Shah
Abstract summary: We focus on ground videos instead of images which provides contextual cues. At clip-level, a short video clip is matched with corresponding aerial image and is later used to get video-level geo-localization of a long video. Our proposed method achieves a Top-1 recall rate of 19.4% and 45.1% @1.0mile.
Score: 68.33955764543465
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The existing work in cross-view geo-localization is based on images where a ground panorama is matched to an aerial image. In this work, we focus on ground videos instead of images which provides additional contextual cues which are important for this task. There are no existing datasets for this problem, therefore we propose GAMa dataset, a large-scale dataset with ground videos and corresponding aerial images. We also propose a novel approach to solve this problem. At clip-level, a short video clip is matched with corresponding aerial image and is later used to get video-level geo-localization of a long video. Moreover, we propose a hierarchical approach to further improve the clip-level geolocalization. It is a challenging dataset, unaligned and limited field of view, and our proposed method achieves a Top-1 recall rate of 19.4% and 45.1% @1.0mile. Code and dataset are available at following link: https://github.com/svyas23/GAMa.

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