Transmission: a new feature for computer vision based smoke detection

Authors:
Chengjiang Long;Jianhui Zhao;Shizhong Han;Lu Xiong;Zhiyong Yuan;Jing Huang;Weiwei Gao
Affiliations:
Computer School, Wuhan University, Wuhan, Hubei, PR China;Computer School, Wuhan University, Wuhan, Hubei, PR China;Computer School, Wuhan University, Wuhan, Hubei, PR China;Computer School, Wuhan University, Wuhan, Hubei, PR China;Computer School, Wuhan University, Wuhan, Hubei, PR China;Computer School, Wuhan University, Wuhan, Hubei, PR China;Computer School, Wuhan University, Wuhan, Hubei, PR China
Venue:
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Year:
2010

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Abstract

A novel and effective approach is proposed in this paper to detect smoke using transmission from image or video frame. Inspired by the airlight-albedo ambiguity model, we introduce the concept of transmission as a new essential feature of smoke, which is employed to detect the smoke and also determine its corresponding thickness distribution. First, we define an optical model for smoke based on the airlight-albedo ambiguity model. Second, we estimate the preliminary smoke transmission using dark channel prior and then refine the result through soft matting algorithm. Finally, we use transmission to detect smoke region by thresholding and obtain detailed information about the distribution of smoke thickness through mapping transmissions of the smoke region into a gray image. Our method has been tested on real images with smoke. Compared with the existing methods, experimental results have proved the better efficiency of transmission in smoke detection.