# A Video-Based Fire Detection Using Deep Learning Models
## 4 Experiments and Results
### Majority Voting and Interpretation of Fire Behavior  
The LSTM short-term fire decisions during \(T_{\mathit{vot}}\) are involved in the majority voting for the final fire decision. Because only short video clips are included in the dataset for comparison in Table 6, we take \(T_{\mathit{vot}}=10\) s for majority voting. Even with such a short-term ensemble, the accuracy increases by up to 97.92%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Video Name** & **Resolution** & **Fames** & **Frame Rate** & **Fire** & **Description** \\ \hline Fire1 & \(320\times 240\) & 705 & 15 & Yes & A fire generated into a bucket and a person walking near it. \\ aFire2 & \(320\times 240\) & 116 & 29 & Yes & A fire very far from the camera generated into a bucket. \\ Fire3 & \(400\times 256\) & 255 & 15 & Yes & A big fire in a forest. \\ Fire4 & \(400\times 256\) & 240 & 15 & Yes & See the notes of the video Fire3. \\ Fire5 & \(400\times 256\) & 195 & 15 & Yes & See the notes of the video Fire3. \\ Fire6 & \(320\times 240\) & 1200 & 10 & Yes & A fire generated in a red ground. \\ Fire7 & \(400\times 256\) & 195 & 15 & Yes & See the notes of the video Fire3. \\ Fire8 & \(400\times 256\) & 240 & 15 & Yes & See the notes of the video Fire3. \\ Fire9 & \(400\times 256\) & 240 & 15 & Yes & See the notes of the video Fire3. \\ Fire10 & \(400\times 256\) & 210 & 15 & Yes & See the notes of the video Fire3. \\ Fire11 & \(400\times 256\) & 210 & 15 & Yes & See the notes of the video Fire3. \\ Fire12 & \(400\times 256\) & 210 & 15 & Yes & See the notes of the video Fire3. \\ Fire13 & \(320\times 240\) & 1,650 & 25 & Yes & A fire in a bucket in indoor environm ent. \\ Fire14 & \(320\times 240\) & 5,535 & 15 & Yes & Fire generated by a paper box. The video has been acquired by the authors near a street. \\ Fire15 & \(320\times 240\) & 240 & 15 & No & Some smoke seen from a closed window. A red reflection of the sun appears on the glass. \\ Fire16 & \(320\times 240\) & 900 & 10 & No & Some smoke pot near a red dust bin. \\ Fire17 & \(320\times 240\) & 1725 & 25 & No & Some smoke on the ground near a moving vehicle and moving trees. \\ Fire18 & \(352\times 288\) & 600 & 10 & No & Some far smoke on a hill. \\ Fire19 & \(320\times 240\) & 630 & 10 & No & Some smoke on a red ground. \\ Fire20 & \(320\times 240\) & 5,958 & 9 & No & Some smoke on a hill with red buildings. \\ Fire21 & \(720\times 480\) & 80 & 10 & No & Some smoke far from the camera behind some moving trees. \\ Fire22 & \(480\times 272\) & 22,500 & 25 & No & Some smoke behind a mountain in front of the university of salerno. \\ Fire23 & \(720\times 576\) & 6,097 & 7 & No & Some smoke above a mountain. \\ Fire24 & \(320\times 240\) & 372 & 10 & No & Some smoke in a room. \\ Fire25 & \(352\times 288\) & 140 & 10 & No & Some smoke far from the camera in a city. \\ Fire26 & \(720\times 576\) & 847 & 7 & No & See the notes of the video Fire24. \\ Fire27 & \(320\times 240\) & 1,400 & 10 & No & See the notes of the video Fire19. \\ Fire28 & \(352\times 288\) & 6,025 & 25 & No & See the notes of the video Fire18. \\ Fire29 & \(720\times 576\) & 600 & 10 & No & Some smoke in a city covering red buildings. \\ Fire30 & \(800\times 600\) & 1,920 & 15 & No & A person moving in a lab holding a red ball. \\ Fire31 & \(800\times 600\) & 1,485 & 15 & No & A person moving in a lab with a red notebook. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The dataset information [11]. Also, we collected an additional 38 video clips from the internet, including Youtube, which have a relatively long playing-time. Table 7 represents the categorized fire/non-fire video clips with their time-varying behaviors. Figure 12 shows samples of the video dataset. We performed the majority voting for the final fire decision and evaluated the accuracy according to the time period of \(T_{vot}\) and the results are summarized in Table 8. Note that the longer time period provides better accuracy in general because more LSTM decisions are combined in the majority voting to make a robust ensemble.