Homography Estimation for Camera Calibration in Complex Topological Scenes

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Abstract

Surveillance videos and images are used for a broad set of applications, ranging from traffic analysis to crime detection. Extrinsic camera calibration data is important for most analysis applications. However, security cameras are susceptible to environmental conditions and small camera movements, resulting in a need for an automated re-calibration method that can account for these varying conditions. In this paper, we present an automated camera-calibration process leveraging a dictionary-based approach that does not require prior knowledge on any camera settings. The method consists of a custom implementation of a Spatial Transformer Network (STN) and a novel topological loss function. Experiments reveal that the proposed method improves the IoU metric by up to 12% w.r.t. a state-of-the-art model across five synthetic datasets and the World Cup 2014 dataset.
Original languageEnglish
Title of host publication2023 IEEE Intelligent Vehicles Symposium (IV)
PublisherInstitute of Electrical and Electronics Engineers
Pages1-8
Number of pages8
ISBN (Electronic)979-8-3503-4691-6
DOIs
Publication statusPublished - 27 Jul 2023
Event34th IEEE Intelligent Vehicles Symposium, IV 2023 - Anchorage, United States
Duration: 4 Jun 20237 Jun 2023

Conference

Conference34th IEEE Intelligent Vehicles Symposium, IV 2023
Abbreviated titleIV 2023
Country/TerritoryUnited States
CityAnchorage
Period4/06/237/06/23

Keywords

  • Camera Calibration
  • Homography Estimation
  • Spatial Transformer Networks
  • Topological Loss
  • camera calibration
  • spatial transformer network
  • warping
  • image matching
  • homography estimation

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