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A Dataset of Annotated Omnidirectional Videos for Distancing Applications
| Content Provider | MDPI |
|---|---|
| Author | Mazzola, Giuseppe Presti, Liliana Lo Ardizzone, Edoardo Cascia, Marco La |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 158 |
| e-ISSN | 2313433X |
| DOI | 10.3390/jimaging7080158 |
| Journal | Journal of Imaging |
| Issue Number | 8 |
| Volume Number | 7 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-08-21 |
| Access Restriction | Open |
| Subject Keyword | Journal of Imaging Artificial Intelligence Omnidirectional Cameras 360° Video Dataset Depth Estimation Distancing Video Surveillance Tracking Pedestrian Equirectangular Projection Spherical Images |
| Content Type | Text |
| Resource Type | Article |
