{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T11:11:17Z","timestamp":1730200277314,"version":"3.28.0"},"reference-count":28,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1109\/bigdata.2018.8622161","type":"proceedings-article","created":{"date-parts":[[2019,1,25]],"date-time":"2019-01-25T03:07:18Z","timestamp":1548385638000},"page":"1142-1149","source":"Crossref","is-referenced-by-count":0,"title":["Speed Accuracy Trade-off in Pedestrian and Vehicle Detection Using Localized Big Data"],"prefix":"10.1109","author":[{"given":"Yeongro","family":"Yun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youngseok","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chanhee","family":"Woo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sejoon","family":"Lim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IHMSC.2016.130"},{"key":"ref11","article-title":"DAVE: a unified framework for fast vehicle detection and annotation","author":"zhou","year":"2016","journal-title":"in European Conference on Computer Vision"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TENCONSpring.2016.7519418"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICDSP.2016.7868561"},{"key":"ref14","first-page":"67","article-title":"Convolutional Neural Network for Person and Car Detection using YOLO Framework","volume":"10","author":"putra","year":"2018","journal-title":"Journal of Telecommunication Electronic and Computer Engineering (JTEC)"},{"article-title":"Frustum pointnets for 3d object detection from rgb-d data","year":"2017","author":"qi","key":"ref15"},{"key":"ref16","article-title":"Microsoft COCO: Common Objects in Context","author":"lin","year":"2014","journal-title":"European Conference on Computer Vision"},{"key":"ref17","article-title":"You only look once: Unified, real-time object detection","author":"redmon","year":"2015","journal-title":"Proc IEEE Conf Computer Vision and Pattern Recognition"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"article-title":"Speed\/accuracy trade-offs for modern convolutional object detectors","year":"2017","author":"jonathan","key":"ref28"},{"article-title":"YOLOv3: An Incremental Improvement","year":"2018","author":"redmon","key":"ref4"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.23919\/ELINFOCOM.2018.8330547"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref6","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref5","article-title":"Ssd: Single shot multibox detector","author":"liu","year":"2016","journal-title":"in European Conference on Computer Vision"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2017.111"},{"key":"ref7","article-title":"Is faster r-cnn doing well for pedestrian detection?","author":"zhang","year":"2016","journal-title":"European Conference on Computer Vision"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1076\/icsp.10.1.53.14103"},{"key":"ref9","article-title":"Pedestrian detection in video surveillance using fully convolutional YOLO neural network","volume":"10334","author":"molchanov","year":"2017","journal-title":"Automated Visual Inspection Machine Vision"},{"key":"ref1","article-title":"Critical reasons for crashes investigated in the national motor vehicle crash causation survey","author":"singh","year":"2015","journal-title":"Traffic Safety Facts Crash Stats"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913491297"},{"article-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","year":"2017","author":"howard","key":"ref22"},{"article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","year":"2015","author":"ioffe","key":"ref21"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","year":"2016","author":"szegedy","key":"ref26"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"}],"event":{"name":"2018 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2018,12,10]]},"location":"Seattle, WA, USA","end":{"date-parts":[[2018,12,13]]}},"container-title":["2018 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8610059\/8621858\/08622161.pdf?arnumber=8622161","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T23:26:01Z","timestamp":1643239561000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8622161\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/bigdata.2018.8622161","relation":{},"subject":[],"published":{"date-parts":[[2018,12]]}}}