Towards a solid solution of real-time fire and flame detection

TL;DR

This paper introduces a robust, real-time fire and flame detection system using a three-step process, validated on a new benchmark dataset, achieving high accuracy and outperforming existing methods.

Contribution

It presents a novel, empirically validated approach for real-time fire detection in videos, including a new dataset, source code, and benchmark for future research.

Findings

Achieved 82% recall and 93% precision on the dataset.

Outperformed state-of-the-art methods in fire detection.

Provided publicly available source code and dataset.

Abstract

Although the object detection and recognition has received growing attention for decades, a robust fire and flame detection method is rarely explored. This paper presents an empirical study, towards a general and solid approach to fast detect fire and flame in videos, with the applications in video surveillance and event retrieval. Our system consists of three cascaded steps: (1) candidate regions proposing by a background model, (2) fire region classifying with color-texture features and a dictionary of visual words, and (3) temporal verifying. The experimental evaluation and analysis are done for each step. We believe that it is a useful service to both academic research and real-world application. In addition, we release the software of the proposed system with the source code, as well as a public benchmark and data set, including 64 video clips covered both indoor and outdoor scenes under different conditions. We achieve an 82% Recall with 93% Precision on the data set, and greatly improve the performance by state-of-the-arts methods.