{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T05:54:06Z","timestamp":1726206846880},"reference-count":14,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2014,11,11]],"date-time":"2014-11-11T00:00:00Z","timestamp":1415664000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Human detection using visible surveillance sensors is an important and challenging work for intruder detection and safety management. The biggest barrier of real-time human detection is the computational time required for dense image scaling and scanning windows extracted from an entire image. This paper proposes fast human detection by selecting optimal levels of image scale using each level\u2019s adaptive region-of-interest (ROI). To estimate the image-scaling level, we generate a Hough windows map (HWM) and select a few optimal image scales based on the strength of the HWM and the divide-and-conquer algorithm. Furthermore, adaptive ROIs are arranged per image scale to provide a different search area. We employ a cascade random forests classifier to separate candidate windows into human and nonhuman classes. The proposed algorithm has been successfully applied to real-world surveillance video sequences, and its detection accuracy and computational speed show a better performance than those of other related methods.<\/jats:p>","DOI":"10.3390\/s141121247","type":"journal-article","created":{"date-parts":[[2014,11,11]],"date-time":"2014-11-11T13:59:22Z","timestamp":1415714362000},"page":"21247-21257","source":"Crossref","is-referenced-by-count":17,"title":["Fast Human Detection for Intelligent Monitoring Using Surveillance Visible Sensors"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-7284-0768","authenticated-orcid":false,"given":"Byoung","family":"Ko","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Keimyung University, Sindang-dong, Dalseo-gu, Daegu 704-701, Korea"}]},{"given":"Mira","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Keimyung University, Sindang-dong, Dalseo-gu, Daegu 704-701, Korea"}]},{"given":"JaeYeal","family":"Nam","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Keimyung University, Sindang-dong, Dalseo-gu, Daegu 704-701, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2014,11,11]]},"reference":[{"key":"ref_1","unstructured":"Schwartz, W.R., Kembhavi, A., Harwood, D., and Davis, L.S. (October, January 29). Human detection using partial least squares analysis. Kyoto, Japan."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tang, D., Liu, Y., and Kim, T.-K. (2012, January 3\u20137). Fast pedestrian detection by cascaded random forest with dominant orientation Tenmplates. Surrey, UK.","DOI":"10.5244\/C.26.58"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Benenson, R., Mathias, M., Timofte, R., and Gool, L.V. (2012, January 16\u201321). Pedestrian detection at 100 frames per second. Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248017"},{"key":"ref_4","unstructured":"Doll\u00e1r, P., Belongie, S., and Perona, P. (September, January 31). The fastest pedestrian detector in the west. Aberystwyth, UK."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liang, F., Wang, D., Liu, Y., Jiang, Y., and Tang, S. (2012, January 4\u20136). Fast pedestrian detection based on sliding window filtering. 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Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.OE.52.11.113105","article-title":"Human tracking in thermal images using adaptive particle filters with online random forest learning","volume":"52","author":"Ko","year":"2013","journal-title":"Opt. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1142\/S0218001403002721","article-title":"Robust face detection and tracking for real-life applications","volume":"17","author":"Byun","year":"2003","journal-title":"Int. J. Patt. Recogn. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_13","unstructured":"Fisher, B. CAVIAR Test Case Scenarios. Available online: http:\/\/homepages.inf.ed.ac.uk\/rbf\/CAVIARDATA1\/."},{"key":"ref_14","unstructured":"Ferryman, J. PETS: Performance Evaluation of Tracking and Surveillance. Available online: ftp:\/\/ftp.cs.rdg.ac.uk\/pub\/PETS2009."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/11\/21247\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,2]],"date-time":"2024-06-02T22:34:42Z","timestamp":1717367682000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/11\/21247"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,11,11]]},"references-count":14,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2014,11]]}},"alternative-id":["s141121247"],"URL":"https:\/\/doi.org\/10.3390\/s141121247","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,11,11]]}}}