Notice of Violation of IEEE Publication Principles: Effective visual fire detection in video sequences using probabilistic approach

Notice of Violation of IEEE Publication Principles

"Effective Visual Fire Detection in Video Sequences Using Probabilistic Approach"
by P. Jenifer
in the 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), 2011, pp. 870 – 875.

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"A Probabilistic Approach for Vision-Based Fire Detection in Videos"
by Paulo Vinicius Koerich Borges and Ebroul Izquierdo
in IEEE Transactions on Circuits and Systems for Video Technology Vol. 5, No. 5, 2010, pp. 721 – 731.


The objective is to develop a probabilistic approach for vision-based fire detection in videos. The proposed method analyzes the frame-to-frame changes of specific low-level features describing potential fire regions. These features are color, area size, surface coarseness, boundary roughness, and skewness within estimated fire regions. Because of flickering and random characteristics of fire, these features are powerful discriminants. The behavioral change of each one of these features is evaluated, and the results are then combined according to the Bayes classifier for robust fire recognition. Temporal matching concept is used to reduce the computational complexity and also to allow fast processing of videos. Early vision-based fire detection techniques target surveillance applications with static cameras and consequently reasonably controlled or static background. In contrast, the proposed method can be applied not only to surveillance but also to automatic video classification for retrieval of fire catastrophes in databases of newscast content.