# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 4 Experimental Results and Discussions
### Experiemts including Bayesian Network
Note that we obtained perfect accuracy using the majority voting method independent of Bayesian inference when the time interval is long enough. This implies that the ensemble of majority voting is as reliable as a human fire detecting, based on its long-time dynamic behavior. Furthermore, the additional Bayesian network is helpful to reduce the time interval for making perfect decisions. Not that the latency is an important factor, because early fire detection is critical not to spread a fire. We want to reiterate that our process is similar to a human's fire decision. If a person were only to rely on the dynamic behavior of local visual information (such as the behavior in SRoF), although they would ultimately reach the correct conclusion, it would take significant time. On the other hand, however, a person accounted for the overall location circumstances where fires happen, along with other environmental information, meaning the conclusion could be reached faster. Table 14 shows the overall analysis results, which includes the changes in the area of smoke (or steam) and flame measured in the number of pixels with temporal changes in the ratio of voting, the Bayesian inference result, the classified place category, and the result of smoke detection for video clips in Figure 12. In our experiment, we set \(T_{\text{\tiny{vol}}}=T_{Bayes}=1\) min and assume the presumable humidity states depending on the content of the video after watching. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Decision**} & \multirow{2}{*}{**1 min**} & \multirow{2}{*}{**1 min 30 s**} & \multirow{2}{*}{**2 min**} & \multirow{2}{*}{**2 min 30 s**} & \multirow{2}{*}{**3 min**} \\
**Interval** & & & & & & \\ \((T_{\text{\tiny{rot}}}=T_{Bayes})\) & & & & & & \\ \hline Majority voting & 96.3\% & 97.8\% & 98.5\% & 99.4\% & 100\% & 100\% \\ Bayesian net & 97.6\% & 98.5\% & 99.3\% & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Accuracy comparison depending on the decision interval and Bayesian network [26]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Fire(GT)**} & \multirow{2}{*}{**Set H**} & \multicolumn{4}{c}{**Visual Analysis**} & \\ \cline{4-7}  & & & **P(F)** & **D** & **S** & **P(F \(|\) \(D\),H,S)** & **Result** \\ \hline (a) & _Yes_ & _Dry_ & _0.6_ & _Yes_ & _No_ & 0.897 & _Yes_ \\ (b) & _No_ & _Wet_ & _0.3_ & _Yes_ & _Yes_ & 0.055 & _No_ \\ (c) & _No_ & _Normal_ & _0.5_ & _No_ & _Yes_ & 0.364 & _No_ \\ (d) & _Yes_ & _Normal_ & _0.8_ & _Yes_ & _Yes_ & 0.903 & _Yes_ \\ (e) & _Yes_ & _Normal_ & _0.6_ & _Yes_ & _Yes_ & 0.777 & _Yes_ \\ (f) & _Yes_ & _Normal_ & _0.8_ & _No_ & _Yes_ & 0.595 & _Yes_ \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Changes in the probability of fire before and after Bayesian inference [26]. Figure 12: Sample still shots taken from video clips for the experiment [26]. The (**a,b**) images are free videos, while the (**c,d**) images non-fire video clips.