Automatic Discovery and Geotagging of Objects from Street View Imagery

Abstract

Many applications such as autonomous navigation, urban planning and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this paper we propose to automatically detect and compute the GPS coordinates of recurring stationary objects of interest using street view imagery. Our processing pipeline relies on two fully convolutional neural networks: the first segments objects in the images while the second estimates their distance from the camera. To geolocate all the detected objects coherently we propose a novel custom Markov Random Field model to perform objects triangulation. The novelty of the resulting pipeline is the combined use of monocular depth estimation and triangulation to enable automatic mapping of complex scenes with multiple visually similar objects of interest. We validate experimentally the effectiveness of our approach on two object classes: traffic lights and telegraph poles. The experiments report high object recall rates and GPS accuracy within 2 meters, which is comparable with the precision of single-frequency GPS receivers.

arXiv commentVideo demo at https://youtu.be/X0tM_iSRJMw

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• SSD: Single Shot MultiBox Detector
• Conditional Random Fields as Recurrent Neural Networks
• Towards seamless multi-view scene analysis from satellite to street-level

Cited by in corpus (5)

• ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Science
• Detecting disparities in police deployments using dashcam data
• Automated Detecting and Placing Road Objects from Street-level Images
• End-to-end Learning Improves Static Object Geo-localization in Monocular Video
• GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end