BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis

TL;DR

This paper introduces BoWFire, a novel method for detecting fire in still images by combining pixel color and texture analysis, reducing false positives and simplifying parameter tuning.

Contribution

It presents a new fire detection approach for still images that integrates color and texture features with fewer parameters, improving accuracy and ease of use.

Findings

Reduces false positives compared to existing methods

Maintains precision comparable to state-of-the-art techniques

Uses fewer parameters for easier fine-tuning

Abstract

Emergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowdsourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions. Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information. There are several methods for fire detection on video using color-based models. However, they are not adequate for still image processing, because they can suffer on high false-positive results. These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task. In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on superpixel regions. Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method. Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.