{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T09:36:17Z","timestamp":1782466577626,"version":"3.54.5"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2017,8,10]],"date-time":"2017-08-10T00:00:00Z","timestamp":1502323200000},"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":"<jats:p>Most of the commercial nighttime pedestrian detection (PD) methods reported previously utilized the histogram of oriented gradient (HOG) or the local binary pattern (LBP) as the feature and the support vector machine (SVM) as the classifier using thermal camera images. In this paper, we propose a new feature called the thermal-position-intensity-histogram of oriented gradient (TPIHOG or T   \u03c0   HOG) and developed a new combination of the T   \u03c0   HOG and the additive kernel SVM (AKSVM) for efficient nighttime pedestrian detection. The proposed T   \u03c0   HOG includes detailed information on gradient location; therefore, it has more distinctive power than the HOG. The AKSVM performs better than the linear SVM in terms of detection performance, while it is much faster than other kernel SVMs. The combined T   \u03c0   HOG-AKSVM showed effective nighttime PD performance with fast computational time. The proposed method was experimentally tested with the KAIST pedestrian dataset and showed better performance compared with other conventional methods.<\/jats:p>","DOI":"10.3390\/s17081850","type":"journal-article","created":{"date-parts":[[2017,8,10]],"date-time":"2017-08-10T10:34:35Z","timestamp":1502361275000},"page":"1850","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Efficient Pedestrian Detection at Nighttime Using a Thermal Camera"],"prefix":"10.3390","volume":"17","author":[{"given":"Jeonghyun","family":"Baek","sequence":"first","affiliation":[{"name":"The School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungjun","family":"Hong","sequence":"additional","affiliation":[{"name":"The School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jisu","family":"Kim","sequence":"additional","affiliation":[{"name":"The School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Euntai","family":"Kim","sequence":"additional","affiliation":[{"name":"The School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1049\/iet-its.2013.0163","article-title":"Night-time Pedestrian Classification with Histograms of Oriented Gradients-Local Binary Patterns Vectors","volume":"9","author":"Hurney","year":"2015","journal-title":"IET Intell. 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