# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 4 Experimental Results and Discussions
### Experiemts including Bayesian Network  
In order to evaluate the performance of Bayesian network, we took the Foggia dataset again and obtained all the visual information, including the results of majority voting, smoke detection, and place classification. The dataset does only contained video clips, and there was little information to infer the state of humidity in the visual contents. So, we set the state of humidity as _\"Normal\"_ and performed the inference by Bayesian network. Table 10 summarizes the results. Figure 10: Typical images for six place classifiers. (**a**) are building, forest or mountain, and street. (**b**) are traffic, etc., and irrelevant [26]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Method** \\ \hline Input size & 1024 \\ Learning rate & 0.01 \\ Learning rate decay & 0.01 (step:50, 150) \\ Weight decay & 0.0004 \\ Batch size & 128 \\ Epoch & 250 \\ Weight initialization & Xavier \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Training Parameters of place classifier [26]. The table represents that our simple Bayesian net increases 0.77% of accuracy, and reduces 0.90% of the false-positive rate. This implies the additional visual information combined with the Bayesian network, such as the place category and the existence of smoke, can help to improve the fire-detection performance, even though the humidity is set to \"Normal\". To sufficiently validate the voting process and Bayesian network, we have collected 40 additional video clips from YouTube which have relatively long-playing times between 4.5 and 6.3 min. Table 11 shows an overview of the fire/non-fire video clips with summarized interpretations of the dynamic behaviors. The set of 11 non-fire video clips consists of fire-like sunset, cloud, and chimney smoke scenes that are easily false detected. Figure 11 shows samples of still shots from the video dataset, which may be useful to longer-term experiments. We picked arbitrarily six video clips as in Figure 11, and observed the changes in the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **False Positive (\%)** & **False Negative (\%)** & **Accuracy (\%)** \\ \hline Proposed method & 2.46 & 1.45 & 97.68 \\ (\(T_{\text{vol}}=3T_{LSTM}\)) [26] & 1.56 & 1.45 & 98.45 \\ (\(T_{Bayes}=T_{\text{vol}}=3T_{LSTM}\)) [26] & 0.00 & 0.14 & 95.86 \\ [PERSON] et al. [21] & 2.33 & 0.84 & 97.94 \\ [PERSON] et al. [24] & 1.23 & 2.25 & 96.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison with other methods. Figure 11: Bayesian network results for 6 cases [26]. The (**a**,**d**–**f**) images are taken from videos of fire, the (**b**,**c**) images are from non-fire video clips. probability of fire before and after inference to check the effectiveness of our Bayesian net. In the experiment, the humidity state was presumed and set to \"_Dry_\",\" Wet\", or \"_Normal_\", depending on the video content after watching, because there was no information on humidity attached to each video clip. Table 12 shows the change in the probabilities. All the results in the table show that a posteriori probability is improved for accurate decision except case 6, in which wrong place decision reduces the probability of fire after Bayesian inference. Note that the place was classified as \"sea\", which is irrelevant to fire. The finely categorized places, according to the domain where the fire detection works, for example, \"ship\", \"harbor\" or \"open sea\" in the case, can remedy the problem. Note that a posteriori probability after Bayesian inference can help to improve the fire decision in this example. For the collected dataset, we extended the period \(T_{\text{\tiny{vol}}}=T_{Bayes}\), because the video clips were long enough. The accuracy depended on the length of the decision period, as shown in Table 13. In the experiment, we set humidity as _Normal_ because there was no information about the humidity.