Title: A fire detection and localisation method based on keyframes and superpixels for large-space buildings

Authors: Qiansheng Fang; Zhuang Peng; Pu Yan; Jing Huang

Addresses: School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230601, China ' School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230601, China ' School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230601, China ' School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230601, China

Abstract: Relevant fire detection and localisation methods suffer from the problems of slow detection speeds, low detection accuracies and low localisation precisions when applied to large-space buildings. To address these problems, a fire detection and localisation method based on keyframes and superpixels is proposed herein. Initially, we improve the SuperPoint method to extract video keyframes; then, we detect and locate fires only on these extracted video keyframes, thus extensively improving the detection speed. Then, we perform fire detection and localisation via superpixel and multi-feature fusion methods applied to the extracted video keyframes, and the results are more accurate than those obtained with single features, thus realising precise localisation. The experimental results obtained using a publicly available fire dataset reveal that our method realises good keyframe extraction, fire detection and fire localisation performances and is thus suitable for detecting and localising fires under large-building surveillance.

Keywords: fire detection; SuperPoint; keyframes; superpixels; multi-feature; fire localisation.

DOI: 10.1504/IJIIDS.2023.128269

International Journal of Intelligent Information and Database Systems, 2023 Vol.16 No.1, pp.1 - 19

Received: 04 Jan 2022
Accepted: 25 May 2022
Published online: 16 Jan 2023 *

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