# A Video-Based Fire Detection Using Deep Learning Models
## 4 Experiments and Results
### Majority Voting and Interpretation of Fire Behavior
However, the dispatch of firemen should be done as early as possible so that \(T_{vot}\) can be adjusted by the trade-off between the accuracy and the critical time for dispatch. We have compared the performances of our scheme in terms of three metrics including false positive, false negative, and accuracy. While the method of [PERSON] et al. [31] performs the best in terms of false positive and false negative, ours with the delayed decision of the majority voting in 10 s outperforms in accuracy. Note that our proposed method can produce this better by introducing the delayed decision in DTA. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **False Positive (\%)** & **False Negative (\%)** & **Accuracy (\%)** \\ \hline Proposed method & & & \\ (hidden unit cell = 512) & 3.04 & 1.73 & 95.00 \\ Proposed method & & & \\ (Majority Voting = 10 s) & 2.47 & 1.38 & **97.92** \\ [PERSON] et al. [14] & 8.87 & 2.12 & 94.50 \\ [PERSON] et al. [11] & 11.67 & 0.00 & 93.55 \\ [PERSON] et al. [27] & 13.33 & 0.00 & 92.86 \\ [PERSON] et al. [28] & 5.88 & 14.29 & 90.32 \\ [PERSON] et al. (YUV color) [29] & 17.65 & 7.14 & 74.20 \\ [PERSON] et al. [5] & 29.41 & 0.00 & 83.87 \\ [PERSON] et al. [7] & 11.76 & 14.29 & 87.1 \\ [PERSON] et al. [30] & 1.23 & 2.25 & 96.53 \\ [PERSON] et al. [31] & **0** & **0.14** & 95.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison with other methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Fire State Change** & **Interpretation** & **Number of Video Clips** \\ \hline Decreasing & Decreasing flame/Increasing smoke or steam & 9 \\ Increasing & Increasing flame & 9 \\ Maintaining & Sustain flame/smoke & 11 \\ Non-fire & False object & 11 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fire detection accuracy of Faster R-CNN. \begin{table}
\begin{tabular}{c c c} \hline \hline
**30 s** & **1 min** & **1 min** & **2 min** \\  & **30 s** & **2 min** & **30 s** \\ \hline
96.73\% & 99.28\% & 99.64\% & 99.94\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy of final fire decision after majority voting with respect to \(T_{vot}\). Also, we monitored the changes in the area of smoke (or steam) and flame. In the experiment we set \(T_{\textit{rep}}=T_{\textit{rot}}=1\) min. Because Faster R-CNN frequently confuses between true smoke and steam, the results include both areas without distinction. In the video clip shown at the first row and the first column, a fire starts with a flame, but a man pours a bottle of water to extinguish the flame as it grows, then steam (or smoke) begins to increase. Figure 13 shows the sample frames of the video sequence and Figure 14 represent the changes in areas (pixels in a frame) of flame and smoke and the final decisions over time. The decision of majority voting starts with fire but then changes into non-fire. Figure 15 represents a sunrise video clip. Even though Faster R-CNN wrongly detects the flame objects and the area of flame increases, the final decisions of majority voting are consistently non-fire, as shown in Figure 16. We obtained similar experimental results for the other 36 video clips in Table 7, which represent the correct final decisions even though Faster R-CNN provides wrong object detection for clouds, steam from man-holes, and sunset video clips. In the video clip of Figure 17, the fire is  
Figure 14: Changes in areas of flame and smoke with final decisions by majority voting for the video clip of Figure 13.  
Figure 12: Sample still shots taken from video clips for the experiment of majority voting and interpretation of dynamic fire behavior: (**a**) real fires, and (**b**) non-fires such as chimney smoke, sunset, cloud, fog, and light. Figure 13: Sample still shots of the video clip shown in the first row and first column.