A dataset of annotated omnidirectional videos for distancing applications

Research output: Contribution to journalArticlepeer-review


Omnidirectional (or 360◦ ) cameras are acquisition devices that, in the next few years, could have a big impact on video surveillance applications, research, and industry, as they can record a spherical view of a whole environment from every perspective. This paper presents two new contributions to the research community: the CVIP360 dataset, an annotated dataset of 360◦ videos for distancing applications, and a new method to estimate the distances of objects in a scene from a single 360◦ image. The CVIP360 dataset includes 16 videos acquired outdoors and indoors, annotated by adding information about the pedestrians in the scene (bounding boxes) and the distances to the camera of some points in the 3D world by using markers at fixed and known intervals. The proposed distance estimation algorithm is based on geometry facts regarding the acquisition process of the omnidirectional device, and is uncalibrated in practice: the only required parameter is the camera height. The proposed algorithm was tested on the CVIP360 dataset, and empirical results demonstrate that the estimation error is negligible for distancing applications.
Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalJournal of Imaging
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering


Dive into the research topics of 'A dataset of annotated omnidirectional videos for distancing applications'. Together they form a unique fingerprint.

Cite this