  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.
