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Concretely, background subtraction is performed with a context-based learning mechanism so as to attain higher accuracy and robustness. The computational cost of a frequency analysis of potential fire regions is reduced by means of focusing its operation with an attentive mechanism. For fast discrimination between fire regions and fire-coloured moving objects, a new colour-based model of fire's appearance and a new wavelet-based model of fire's frequency signature are proposed. To reduce the false alarm rate due to the presence of fire-coloured moving objects, the category and behaviour of each moving object is taken into account in the decision-making. To estimate the expected object's size in the image plane and to generate geo-referenced alarms, the camera-world mapping is approximated with a GPS-based calibration process. Experimental results demonstrate the ability of the proposed method to detect fires with an average success rate of 93.1% at a processing rate of 10 Hz, which is often sufficient for real-life applications. <\/jats:p>","DOI":"10.5772\/58821","type":"journal-article","created":{"date-parts":[[2014,9,19]],"date-time":"2014-09-19T15:52:33Z","timestamp":1411141953000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":37,"title":["A Vision-Based Approach to Fire Detection"],"prefix":"10.1177","volume":"11","author":[{"given":"Pedro","family":"Gomes","sequence":"first","affiliation":[{"name":"CTS-UNINOVA, Universidade Nova de Lisboa, Portugal"}]},{"given":"Pedro","family":"Santana","sequence":"additional","affiliation":[{"name":"ISCTE - Instituto Universit\u00e1rio de Lisboa (ISCTE-IUL), Instituto de Telecomunica\u00e7\u00f5es, Portugal"}]},{"given":"Jos\u00e9","family":"Barata","sequence":"additional","affiliation":[{"name":"CTS-UNINOVA, Universidade Nova de Lisboa, Portugal"}]}],"member":"179","published-online":{"date-parts":[[2014,1,1]]},"reference":[{"key":"bibr1-58821","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(01)00135-0"},{"key":"bibr2-58821","unstructured":"Chen T.H., Kao C.L., and Chang S.M. 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