Towards a solid solution of real-time fire and flame detection
This addresses fire detection for video surveillance and event retrieval, but it is incremental as it builds on existing object detection methods.
The paper tackles the problem of real-time fire and flame detection in videos by proposing a cascaded system using background modeling, color-texture features, and temporal verification, achieving 82% recall and 93% precision on a new dataset.
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.