# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
## 3. Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Classification** & **False Positives** & **False Negatives** & **Activation Time** \\ \hline Proposed & 94.77\% & 4.40\% & 0.83\% & 7.76 s \\ Yuan & 92.63\% & 2.26\% & 5.11\% & 19.26 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the proposed method and [PERSON]’s method using only the texture feature. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Average Classification** & **False Positives** & **False Negatives** \\ \hline MLP\_BP & 92.80\% & 2.30\% & 4.90\% \\ SVM & 92.14\% & 5.00\% & 2.85\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of a database with two classifiers using only the texture feature. Tables 4 and 5 show the smoke texture evaluation results obtained using the videos with smoke and those without smoke, respectively. In both situations, 700 images with smoke and 700 without smoke were used, each with \(22\times 22\) pixels. Evaluation results show that the proposed system provided false positive and false negative rates lower that 1% and a detection rate higher than 98%. In addition, the proposed system activated the alarm in about 6 s when the video contained smoke, while it was not activated in the case of smokeless videos. Here, the false positives were caused by image blocks with smoke textures that were not connected to regions that grow or that appeared and disappeared over the time, thus did not activate the alarm. On the other hand, the false negatives were due to blocks with smoke, the color of which was almost transparent and the characterized texture corresponded mainly to backward objects. Table 6 shows some metrics of the algorithm performance. The alarm activation time depends on the threshold value \(\varepsilon\), which is selected to determine if there is smoke in the scene. The value of \(\varepsilon\) depends on the desired number of frames required to activate the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline
98.79\% & 98.15\% & 99.42\% & 98.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: General metrics of the algorithm performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Smoke** & **Frame** & **Smoke** & **False** & **False** & **Alarm** & **Activation** \\
**Videos** & **Rate (FPS)** & **Classification** & **Positives** & **Negatives** & **Activation** & **Time** \\ \hline Bard & 30 & 98.75\% & 0\% & 1.25\% & YES & 4 s \\ Dump & 10 & 98.58\% & 1.42\% & 0\% & YES & 6.5 s \\ House & 30 & 98.45\% & 0.83\% & 0.71\% & YES & 4 s \\ Trailer & 12 & 99.68\% & 0.19\% & 0.23\% & YES & 4 s \\ Window & 15 & 98.64\% & 0.62\% & 0.74\% & YES & 13.5 s \\ Grill & 25 & 98.81\% & 0.94\% & 0.25\% & YES & 4 s \\ \hline Average & – & 98.81\% & 0.66\% & 0.53\% & – & 6 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percentages texture classification with smoke and smoke detection alarm activation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Videos** & **Frame** & **Classification** & **False** & **False** & **Alarm** \\
**without Smoke** & **Rate (FPS)** & **without Smoke** & **Positives** & **Negatives** & **Activation** \\ \hline Gray Car & 30 & 97.85\% & 2.15\% & 0\% & NO \\ Day Count & 30 & 99.43\% & 0.57\% & 0\% & NO \\ Night Count & 30 & 99.83\% & 0.16\% & 0\% & NO \\ Pigeons & 30 & 99.02\% & 0.98\% & 0\% & NO \\ Tunnel Lopez & 25 & 99.73\% & 0.27\% & 0\% & NO \\ Night Car & 10 & 97.76\% & 2.23\% & 0\% & NO \\ \hline Average & – & 98.94\% & 1.06\% & 0\% & – \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Percentage of smokeless texture classification. Figure 7: Videos used in the testing of smoke. The first line shows videos with smoke and the second line smokeless videos. fire alarm. If we consider a threshold that is too small, the system may produce a large number of false alarms.