Flame recognition in video

    Authors

    W. Phillips; M. Shah;N. D. Lobo

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Pattern Recognit. Lett.

    Keywords

    fire detection; color predicate; skin detection; change detection; Computer Science, Artificial Intelligence

    Abstract

    This paper presents an automatic system for fire detection in video sequences. There are several previous methods to detect fire, however, all except two use spectroscopy or particle sensors. The two that use visual information suffer from the inability to cope with a moving camera or a moving scene. One of these is not able to work on general data, such as movie sequences. The other is too simplistic and unrestrictive in determining what is considered fire; so that it can be used reliably only in aircraft dry bays. We propose a system that uses color and motion information computed from video sequences to locate fire. This is done by first using an approach that is based upon creating a Gaussian-smoothed color histogram to detect the fire-colored pixels, and then using a temporal variation of pixels to determine which of these pixels are actually fire pixels. Next, some spurious fire pixels are automatically removed using an erode operation, and some missing fire pixels are found using region growing method. Unlike the two previous vision-based methods for fire detection, our method is applicable to more areas because of its insensitivity to camera motion. Two specific applications not possible with previous algorithms are the recognition of fire in the presence of global camera motion or scene motion and the recognition of fire in movies for possible use in an automatic rating system. We show that our method works in a variety of conditions, and that it can automatically determine when it has insufficient information. (C) 2002 Published by Elsevier Science B.V.

    Journal Title

    Pattern Recognition Letters

    Volume

    23

    Issue/Number

    1-3

    Publication Date

    1-1-2002

    Document Type

    Article

    Language

    English

    First Page

    319

    Last Page

    327

    WOS Identifier

    WOS:000172398200032

    ISSN

    0167-8655

    Share

    COinS