Computer vision based method for real-time fire and flame detection

Abstract

This paper proposes a novel method to detect fire and/or flames in real-time by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker is detected by analyzing the video in the wavelet domain. Quasi-periodic behavior in flame boundaries is detected by performing temporal wavelet transform. Color variations in flame regions are detected by computing the spatial wavelet transform of moving fire-colored regions. Another clue used in the fire detection algorithm is the irregularity of the boundary of the fire-colored region. All of the above clues are combined to reach a final decision. Experimental results show that the proposed method is very successful in detecting fire and/or flames. In addition, it drastically reduces the false alarms issued to ordinary fire-colored moving objects as compared to the methods using only motion and color clues. © 2005 Elsevier B.V. All rights reserved

Flame Detection for Video-Based Early Fire Warning for the Protection of Cultural Heritage

“…Since we require three neighbouring blocks to reach the highest confidence level, the fire should occupy a region of size 32 by 32 in video. The proposed method is compared with one of our previous fire detection method [11]. In the decision process, if the confidence level of any block of the frame is greater than or equal than 3, then that frame is marked as a fire containing frame.…”

“…In the decision process, if the confidence level of any block of the frame is greater than or equal than 3, then that frame is marked as a fire containing frame. The method described in [11] has a similar confidence level metric to determine the alarm level. Results are summarized in Table 1 in terms of the true detection and the false alarm ratios, respectively.…”

“…Some of the positive videos in the test set are recorded with hand-held moving cameras and since the old method assumes a stationary camera for background subtraction it cannot correctly classify most of the actual fire regions. Comparison of the proposed method with the previous method proposed in [11] in terms of true detection rates in video clips that contain fire and false alarm rates in video clips that do not contain fire. …”

Smoke Detection Using GMM and Adaboost

“…One is to use the color information of the flame and smoke, and the other one uses a dynamic movement [1], [2]. Erroneous detection occurs according to a similar color or lighting color in the surrounding environment.…”

A Study on Fire Detection System using Statistic Color Model

“…Others adopted panoramic camera, wavelet transform, neural networks, etc. for relatively complicated fire detection systems [6][7][8]. And some were very specific purposed systems like tunnel fire, ship compartmentfire, forest fire, etc [9][10][11].…”