Unsupervised Segmentation of Fire and Smoke From Infra-Red Videos

Meenu Ajith,Manel Martinez-Ramon

doi:10.1109/access.2019.2960209

Meenu Ajith, Manel Martinez-Ramon

Open Access

https://doi.org/10.1109/access.2019.2960209

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Abstract

This paper proposes a vision-based fire and smoke segmentation system which uses spatial, temporal and motion information to extract the desired regions from the video frames. The fusion of information is done using multiple features such as optical flow, divergence and intensity values. These features extracted from the images are used to segment the pixels into different classes in an unsupervised way. A comparative analysis is done by using multiple clustering algorithms for segmentation. Here the Markov Random Field performs more accurately than other segmentation algorithms since it characterizes the spatial interactions of pixels using a finite number of parameters. It builds a probabilistic image model that selects the most likely labeling using the maximum a posteriori (MAP) estimation. This unsupervised approach is tested on various images and achieves a frame-wise fire detection rate of 95.39%. Hence this method can be used for early detection of fire in real-time and it can be incorporated into an indoor or outdoor surveillance system.

Highlights

The fire and smoke detectors are an important part of firefighting systems and are widely used in monitoring indoor buildings and outside environments
The feature extraction methods proposed for this problem are by using optical flow, divergence, and intensity
Even though SIFT flow features were tested but it did not give any significant improvement in segmentation compared to the combination of the above feature extractors

Summary

INTRODUCTION

The fire and smoke detectors are an important part of firefighting systems and are widely used in monitoring indoor buildings and outside environments. The vision-based systems either utilize the color information of fire and smoke or it uses the dynamic motion features [1], [2]. This work is intended to discriminate fire (meaning the fire flames present in the scene), smoke and background in thermal imaging sequences The novelty of this approach is the following:. The divergence and optical flow features are chosen since they calculate the flow at a given point and the displacement of pixels from one frame to another These motion features are combined with the intensity values representing the variations in temperature to form the most significant feature vector for segmentation. The latent variable is already known and for this experiment, it can have 3 values corresponding to fire, smoke and background Since these unsupervised algorithms support pre-training, it can be used in real-time for testing. The feature extraction allows fusing information from different sensors and by using this information the fire, smoke, and background can be accurately classified

RELATED WORKS

SEGMENTATION

EXPERIMENTS AND RESULTS

SAMPLE SEGMENTATION COMPARISON USING DIFFERENT ALGORITHMS AND FEATURE VECTORS

CONCLUSION