Unveiling smoke in social images with the SmokeBlock approach.
SAC 2016: Symposium on Applied Computing Pisa Italy April, 2016(2016)
摘要
Can we use information from social media and crowdsourced images to detect smoke and assist rescue forces? While there are computer vision methods for detecting smoke, they require movement information extracted from video data. In this paper we propose SmokeBlock: a method that is able to segment and detect smoke in still images. SmokeBlock uses superpixel segmentation and extracts local color and texture features from images to spot smoke. We used real data from Flickr and compared SmokeBlock against state-of-the-art methods for feature extraction. Our method achieved performance superior than the competitors, for the task of smoke detection. Our findings shall support further investigations in the field of image analysis, in particular, concerning images captured with mobile devices.
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