{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:32:41Z","timestamp":1778085161661,"version":"3.51.4"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T00:00:00Z","timestamp":1559347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T00:00:00Z","timestamp":1559347200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T00:00:00Z","timestamp":1559347200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,6]]},"DOI":"10.1109\/iscc47284.2019.8969620","type":"proceedings-article","created":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T05:16:57Z","timestamp":1580447817000},"page":"1-6","source":"Crossref","is-referenced-by-count":31,"title":["Counting Vehicles with Deep Learning in Onboard UAV Imagery"],"prefix":"10.1109","author":[{"given":"Giuseppe","family":"Amato","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luca","family":"Ciampi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabrizio","family":"Falchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudio","family":"Gennaro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Improving object counting with heatmap regulation","author":"aich","year":"2018","journal-title":"arXiv preprint arXiv 1803 05494"},{"key":"ref11","article-title":"Class-agnostic counting","author":"lu","year":"2018","journal-title":"arXiv preprint arXiv 1811 00472"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.10.055"},{"key":"ref13","article-title":"Counting vehicles with cameras","author":"ciampi","year":"2018","journal-title":"Proceedings of the 26th Italian Symposium on Advanced Database Systems Castellaneta Marina"},{"key":"ref14","first-page":"88","article-title":"Shuffledet: Real-time vehicle detection network in onboard embedded uav imagery","author":"azimi","year":"2018","journal-title":"European Conference on Computer Vision"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.02.009"},{"key":"ref16","article-title":"Yolov3: An incremental improvement","author":"redmon","year":"2018","journal-title":"arXiv preprint arXiv 1804 02767"},{"key":"ref17","first-page":"740","article-title":"Microsoft coco: Common objects in context","author":"lin","year":"2014","journal-title":"European Conference on Computer Vision"},{"key":"ref18","article-title":"Faster R-CNN: towards real-time object detection with region proposal networks","volume":"abs 1506 1497","author":"ren","year":"2015","journal-title":"CoRR"},{"key":"ref19","article-title":"A large contextual dataset for classification, detection and counting of cars with deep learning","volume":"abs 1609 4453","author":"mundhenk","year":"2016","journal-title":"CoRR"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.446"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2017.07.007"},{"key":"ref6","first-page":"483","article-title":"Counting in the wild","author":"arteta","year":"2016","journal-title":"European Conference on Computer Vision"},{"key":"ref5","first-page":"1324","article-title":"Learning to count objects in images","author":"lempitsky","year":"2010","journal-title":"Advances in neural information processing systems"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1080\/21681163.2016.1149104","article-title":"Microscopy cell counting and detection with fully convolutional regression networks","volume":"6","author":"xie","year":"2016","journal-title":"Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.70"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.396"},{"key":"ref20","article-title":"Deep learning-based multiple object visual tracking on embedded system for iot and mobile edge computing applications","author":"blanco-filgueira","year":"2018","journal-title":"arXiv preprint arXiv 1808 02194"}],"event":{"name":"2019 IEEE Symposium on Computers and Communications (ISCC)","location":"Barcelona, Spain","start":{"date-parts":[[2019,6,29]]},"end":{"date-parts":[[2019,7,3]]}},"container-title":["2019 IEEE Symposium on Computers and Communications (ISCC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8961334\/8969568\/08969620.pdf?arnumber=8969620","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T17:10:00Z","timestamp":1657991400000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8969620\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/iscc47284.2019.8969620","relation":{},"subject":[],"published":{"date-parts":[[2019,6]]}}}