# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 4 Experimental Results and Discussions
### Training Place Classifier and Its Performance  
For training and testing of the location classifier, we selected data from well-known datasets, including the Intel scene classification challenge dataset [33], the scene under  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Method** \\ \hline Input size & 1024 \\ Time Step & 60 \\ LSTM cell unit & 128/256/512/1024 \\ Learning rate & 0.0001 \\ Weight decay & 0.0004 \\ Batch size & 256 \\ Epoch & 200 \\ Weight initialization & Xavier \\ Dropout & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Training Parameters of LSTM [26].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **False** & **False** \\  & **Positive (\%)** & **Negative (\%)** & **Accuracy (\%)** \\ \hline Proposed method & 3.04 & 1.72 & 95.00 \\ (hidden cell unit = 512) & & \\ Proposed method & 2.46 & 1.45 & 97.68 \\ (\(T_{\text{\emph{rot}}}=3T_{LSTM}\)) & 0.00 & 0.14 & 95.86 \\ [PERSON] et al. [12] & 11.67 & 0.00 & 93.55 \\ [PERSON] et al. [30] & 13.33 & 0.00 & 92.86 \\ [PERSON] et al. [31] & 5.88 & 14.29 & 90.32 \\ [PERSON] et al. (YUV) [32] & 17.65 & 7.14 & 74.20 \\ [PERSON] et al. [6] & 29.41 & 0.00 & 83.87 \\ [PERSON] et al. [8] & 11.76 & 14.29 & 87.1 \\ [PERSON] et al. [22] & 2.33 & 0.84 & 97.94 \\ [PERSON] et al. [24] & 1.23 & 2.25 & 96.53 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison with other methods [26].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **False** & **False** & **Accuracy (\%)** \\  & **Positive (\%)** & **Negative (\%)** & **Accuracy (\%)** \\ \hline Proposed method & 3.04 & 1.72 & 95.00 \\ (hidden cell unit = 512) & & \\ Proposed method & 2.46 & 1.45 & 97.68 \\ (\(T_{\text{\emph{rot}}}=3T_{LSTM}\)) & 0.00 & 0.14 & 95.86 \\ [PERSON] et al. [12] & 11.67 & 0.00 & 93.55 \\ [PERSON] et al. [30] & 13.33 & 0.00 & 92.86 \\ [PERSON] et al. [31] & 5.88 & 14.29 & 90.32 \\ [PERSON] et al. (YUV) [32] & 17.65 & 7.14 & 74.20 \\ [PERSON] et al. [6] & 29.41 & 0.00 & 83.87 \\ [PERSON] et al. [8] & 11.76 & 14.29 & 87.1 \\ [PERSON] et al. [22] & 2.33 & 0.84 & 97.94 \\ [PERSON] et al. [24] & 1.23 & 2.25 & 96.53 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison with other methods [26].  
standing (SUN) dataset [34], the ImageNet dataset [35], among others [36; 37; 38]. We also included frames from 70 video clips from YouTube that contained a sunset, a bonfire, and a dumping ground. The total number of images was 330,000, of which about 55,000 images were included for each place category. For the place classifier, 70% of the data were selected for training, 10% for validation, and 20% for testing. Figure 10 shows typical images for location categorization. As explained in Section 3.3, 1024 dimensional features were extracted to feed into the softmax classifier for place classifier. Table 9 shows the hyper parameters for training the place classifier.  
The accuracy was 96.12% (of Top-1) and 98.24% (of Top-2) for six categories. The Top-1 accuracy of two classes--\"fire-prone\" and \"irrelevant to fire\"--was 98.93%, which is meaningful for deciding the binary states of \(D\) in our Bayesian net.