https://scholars.lib.ntu.edu.tw/handle/123456789/489039
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Lin, Yu-Chun | en_US |
dc.contributor.author | Chan, Yi-Ming | en_US |
dc.contributor.author | Chuang, Luo-Chieh | en_US |
dc.contributor.author | LI-CHEN FU | en_US |
dc.contributor.author | Huang, Shih-Shinh | en_US |
dc.contributor.author | Hsiao, Pei-Yung | en_US |
dc.contributor.author | Luo, Min-Fang | en_US |
dc.creator | Lin, Yu-Chun;Chan, Yi-Ming;Chuang, Luo-Chieh;Fu, Li-Chen;Huang, Shih-Shinh;Hsiao, Pei-Yung;Luo, Min-Fang | - |
dc.date.accessioned | 2020-05-04T08:00:56Z | - |
dc.date.available | 2020-05-04T08:00:56Z | - |
dc.date.issued | 2011 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/489039 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-83755228815&doi=10.1109%2fITSC.2011.6083015&partnerID=40&md5=57451582945c601007b4274fb49ede68 | - |
dc.description.abstract | Pedestrian detection is important in the computer vision field. In the nighttime, pedestrian detection is even more valuable. In this paper, we address the issue of detecting pedestrians in video streams from a moving camera at nighttime. Most nighttime human detection approaches only use single feature extracted from images. The effective image features in daytime environment may suffer from textureless, high contrast and low light problems at night. To deal with these issues, we first segment the foreground by using the proposed Smart Region Detection approach to generate candidates. Then we design a nighttime pedestrian detection system based on the AdaBoost and the support vector machine (SVM) classifiers with contour and histogram of oriented gradients (HOG) features to effectively recognize pedestrians from those candidates. Combining different type of complementary features improve the detection performance. Results show that our pedestrian detection system is promising in the nighttime environment. © 2011 IEEE. | - |
dc.relation.ispartof | IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC | - |
dc.subject | Contour; Detection; HOG; Human; Nighttime; Pedestrian; SVM | - |
dc.subject.other | Contour; HOG; Human; Nighttime; Pedestrian; SVM; Adaptive boosting; Computer vision; Error detection; Feature extraction; Intelligent systems; Support vector machines | - |
dc.title | Near-infrared based nighttime pedestrian detection by combining multiple features. | en_US |
dc.type | conference paper | en |
dc.relation.conference | 14th IEEE International Intelligent Transportation Systems Conference, ITSC 2011 | - |
dc.identifier.doi | 10.1109/ITSC.2011.6083015 | - |
dc.identifier.scopus | 2-s2.0-83755228815 | - |
dc.relation.pages | 1549-1554 | - |
item.cerifentitytype | Publications | - |
item.fulltext | no fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.openairetype | conference paper | - |
item.grantfulltext | none | - |
crisitem.author.dept | Electrical Engineering | - |
crisitem.author.dept | Computer Science and Information Engineering | - |
crisitem.author.dept | Center for Artificial Intelligence and Advanced Robotics | - |
crisitem.author.orcid | 0000-0002-6947-7646 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
顯示於: | 資訊工程學系 |
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