Saliency Based Fire Detection Using Texture and Color Features

TL;DR

This paper presents a video-based fire detection method that combines saliency, color, and texture features to improve accuracy and reduce false positives in early fire warning systems.

Contribution

It introduces a novel approach using saliency maps with HSV color and LBP-TOP texture features for more reliable fire detection in videos.

Findings

High accuracy on public datasets

Robust detection with low false positives

Effective use of spatial-temporal features

Abstract

Due to industry deployment and extension of urban areas, early warning systems have an essential role in giving emergency. Fire is an event that can rapidly spread and cause injury, death, and damage. Early detection of fire could significantly reduce these injuries. Video-based fire detection is a low cost and fast method in comparison with conventional fire detectors. Most available fire detection methods have a high false-positive rate and low accuracy. In this paper, we increase accuracy by using spatial and temporal features. Captured video sequences are divided into Spatio-temporal blocks. Then a saliency map and combination of color and texture features are used for detecting fire regions. We use the HSV color model as a spatial feature and LBP-TOP for temporal processing of fire texture. Fire detection tests on publicly available datasets have shown the accuracy and robustness of the algorithm.