Paper Info
Title
Object Tracking and Geo-Localization from Street Images
Abstract
Object geo-localization from images is crucial to many applications such as land surveying, self-driving, and asset management. Current visual object geo-localization algorithms suffer from hardware limitations and impractical assumptions limiting their usability in real-world applications. Most of the current methods assume object sparsity, the presence of objects in at least two frames, and most importantly they only support a single class of objects. In this paper, we present a novel two-stage technique that detects and geo-localizes dense, multi-class objects such as traffic signs from street videos. Our algorithm is able to handle low frame rate inputs in which objects might be missing in one or more frames. We propose a detector that is not only able to detect objects in images, but also predicts a positional offset for each object relative to the camera GPS location. We also propose a novel tracker algorithm that is able to track a large number of multi-class objects. Many current geo-localization datasets require specialized hardware, suffer from idealized assumptions not representative of reality, and are often not publicly available. In this paper, we propose a public dataset called ARTSv2, which is an extension of ARTS dataset that covers a diverse set of roads in widely varying environments to ensure it is representative of real-world scenarios. Our dataset will both support future research and provide a crucial benchmark for the field.
Year
DOI
Venue
2022
10.3390/rs14112575
REMOTE SENSING
Keywords
DocType
Volume
deep learning, object geo-localization, object detection, object tracking, traffic sign dataset
Journal
14
Issue
ISSN
Citations 
11
2072-4292
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Daniel Wilson100.34
Thayer Alshaabi200.34
Colin Van Oort300.34
Xiaohan Zhang456.83
Jonathan Nelson500.34
Safwan Wshah601.35