{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T06:43:37Z","timestamp":1740120217510,"version":"3.37.3"},"reference-count":17,"publisher":"World Scientific Pub Co Pte Ltd","issue":"05","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["Grant No. 61602223, Grant No. 61502168"],"award-info":[{"award-number":["Grant No. 61602223, Grant No. 61502168"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2018,5]]},"abstract":"<jats:p> Fire detection technology aroused people\u2019s attention increasingly. The main challenge of the fire detection systems is how to reduce false alarms caused by objects like fire\u2019s colors. Most existing algorithms used only features of fire in visual field. In this work, we put forward a new algorithm to detect dynamic fire from the surveillance video based on the combination of radiation domain features model. First, a fire color model is used to extract flame-like pixels as candidate areas in YCbCr space. Second, we convert the candidate regions from the traditional color space into radiation domain in advance by camera calibration. And we use seven features to model the spectral spatio-temporal model of the fire to more accurately characterize the physical and optical properties of the fire. Finally, we choose a two-class SVM classifier to identify the fire from the candidate areas and use a radial basis function kernel to improve the accuracy of the recognition. Two different sets of data are used to validate the algorithm we proposed. And the experimental results indicate that our method performs well in video fire surveillance. <\/jats:p>","DOI":"10.1142\/s0218001418500131","type":"journal-article","created":{"date-parts":[[2017,9,7]],"date-time":"2017-09-07T22:44:56Z","timestamp":1504824296000},"page":"1850013","source":"Crossref","is-referenced-by-count":9,"title":["Spectral Spatio-Temporal Fire Model for Video Fire Detection"],"prefix":"10.1142","volume":"32","author":[{"given":"Zhaohui","family":"Wu","sequence":"first","affiliation":[{"name":"China Academy of Transportation Sciences, Beijing, 100029, P. R. China"},{"name":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Song","sequence":"additional","affiliation":[{"name":"Information Engineering Academy, Zhengzhou University, Zhenzhou, 450001, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobo","family":"Wu","sequence":"additional","affiliation":[{"name":"China Academy of Transportation Sciences, Beijing, 100029, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuqiang","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Baodong, 071003, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, P. R. 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