Saliency Based Fire Detection Using Texture and Color Features
This work addresses early fire detection for safety in industrial and urban settings, but it is incremental as it builds on existing feature-based methods.
The paper tackles the problem of high false-positive rates and low accuracy in video-based fire detection by using spatial and temporal features, including saliency maps, HSV color, and LBP-TOP texture, achieving improved accuracy and robustness on public datasets.
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.