# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
## 3. Results  
To evaluate the effectiveness, robustness and efficiency of the proposed system, several smoke detections were performed using standard databases [15; 16]. To this end, two supervised classifiers, namely _MLP_BP_ and SVM, were used, because, when the input and output are known, the neural network and SVM are very efficient [17]. _MLP_BP_ was used with different inputs and outputs depending on the experiment, using a _tansig_ (hyperbolic tangent sigmoid transfer function), 30 hidden layer neurons, 0.05 minimum error and 1000 iterations in the case of non-convergence. The SVM was configured with a Gaussian radial basis and default values in the library libsvm [18]. In the first test only, the texture classification capability of [PERSON]'s method [14] and the proposed scheme, given by Equation (34) with mean value \(\mu^{I}\) and standard deviation \(\sigma^{I}\) of the pixel's intensity, were compared. Two multilayer perceptron back propagation neural networks (_MLP_BP_) were trained with 1000 images that contain smoke and 1000 that contain different textures, segmented into  
Figure 6. Upward expansion of smoke. square blocks of 48 pixels, while 840 images not used during training, 300 containing smoke and 540 not containing smoke, were used for testing. The evaluation results obtained are shown in Table 2.  
\[V=\{H_{8,1}^{riu2},log(HV_{8,1}^{riu2}),H_{8,2,5}^{riu2},log(HV_{8,2}^{riu2}),\mu^ {I},\sigma^{I}\} \tag{34}\]  
Table 2 shows that the proposed system presented a higher classification rate and less time activation than the scheme proposed by [PERSON] [2], which uses a three-level pyramid decomposition reducing the image size at each level resulting in a characteristic vector of 210 elements. On the other hand, in the proposed system, only two levels are used without image sub-sampling and a characteristic vector of 42 items. In the second test, 2800 images of \(22\times 22\) pixels divided into two groups were used, 1400 images containing smoke and 1400 not containing smoke. These dimensions were chosen because this image size has a good statistical consistency and is less prone to noise from other textures captured in a larger block. In total, 700 images with smoke and 700 smokeless ones were used for training and the remaining 1400 were used for testing. _MLP_BP_ and SVM were used, together with the kernel of Gaussian radial basis function, using only the texture. Evaluation results provided in Table 3 show that a similar performance was obtained using both classifiers. For the image description, Equation (35) was used, reducing the size of the feature vector because only 12 elements were required. \[V=\{H_{8,1}^{riu}+log(HV_{8,1}^{riu2})+H_{8,2,5}^{riu2}+log(HV_{8,2}^{riu2}), \mu^{I},\sigma^{I}\} \tag{35}\]  
The third test was a full evaluation of the proposed algorithm, i.e., images that meet the three characteristics of smoke described above. Movement, color and texture were considered to look for candidate regions to smoke. These regions were then monitored for a certain time looking for upward expansions. If any of these regions maintained an upward growth during a certain time, it was considered as a smoke region and then the alarm was activated. To obtain the evaluation results shown in Table 3, the _MLP_BP_ was trained using 250 images with smoke and 250 smokeless images of size \(22\times 22\) pixels whose features vector were obtained using Equation (35). These 500 images were obtained from the 12 videos shown in Figure 7. The performance of the proposed algorithm was evaluated using six videos with smoke and six smokeless videos, which are available at [12; 13]. Figure 7 shows some of the videos used in the evaluation. The first line shows the videos with smoke and the second line the videos without smoke. Here, the smokeless scenarios had properties and characteristics similar to those with smoke, such as color, texture or upward movement.