Object Tracking and Geo-Localization from Street Images

Object Tracking and Geo-Localization from Street Images

Blog Article

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, Infinity Heart-Shaped Acrylic Plaque 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 ALOE VERA GELLY 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.