{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T15:38:35Z","timestamp":1774021115348,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,21]],"date-time":"2021-08-21T00:00:00Z","timestamp":1629504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Omnidirectional (or 360\u00b0) 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\u00b0 videos for distancing applications, and a new method to estimate the distances of objects in a scene from a single 360\u00b0 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.<\/jats:p>","DOI":"10.3390\/jimaging7080158","type":"journal-article","created":{"date-parts":[[2021,8,22]],"date-time":"2021-08-22T21:47:52Z","timestamp":1629668872000},"page":"158","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Dataset of Annotated Omnidirectional Videos for Distancing Applications"],"prefix":"10.3390","volume":"7","author":[{"given":"Giuseppe","family":"Mazzola","sequence":"first","affiliation":[{"name":"Dipartimento di Ingegneria, Universit\u00e0 degli Studi di Palermo, 90128 Palermo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0833-4403","authenticated-orcid":false,"given":"Liliana","family":"Lo Presti","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria, Universit\u00e0 degli Studi di Palermo, 90128 Palermo, Italy"}]},{"given":"Edoardo","family":"Ardizzone","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria, Universit\u00e0 degli Studi di Palermo, 90128 Palermo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8766-6395","authenticated-orcid":false,"given":"Marco","family":"La Cascia","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria, Universit\u00e0 degli Studi di Palermo, 90128 Palermo, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1109\/TVCG.2017.2657178","article-title":"Mr360: Mixed reality rendering for 360 panoramic videos","volume":"23","author":"Rhee","year":"2017","journal-title":"IEEE Trans. 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