Real‐time multi‐feature based fire flame detection in video

TLDR

For a fire detection system, it is desired to be sensitive and reliable. In this study, the authors present a new approach to detect fire flame by processing and analysing stationary camera videos. The proposed approach uses multi‑feature analysis—chromatic, dynamic, texture, and contour features—along with a novel algorithm that extracts moving regions and analyzes flicker frequency to improve sensitivity and reliability. Experimental results show that the method runs in real time, achieves higher accuracy on fire videos, reduces false alarms on non‑fire videos, and outperforms state‑of‑the‑art methods with faster response time.

Abstract

In this study, the authors present a new approach to detect fire flame by processing and analysing the stationary camera videos. For a fire detection system, it is desired to be sensitive and reliable. The proposed method improves not only the sensitivity but also the reliability through reducing the susceptibility to false alarms. The proposed approach based on multi‐feature, i.e. chromatic features, dynamic features, texture features, and contour features, can both improve the sensitivity and reliability in fire detection. In their approach, the authors adopt a novel algorithm to extract the moving region and analyse the frequency of flickers. Experimental results show that the proposed method can run in real‐time and performs favourably against the state‐of‐the‐art methods with higher accuracy in fire videos, lower false alarm rates in non‐fire videos and faster response time.