# HelpResponder--System for the Security of First Responder Interventions
## 7 Conclusions  
In this paper, we described a system to solve the scientific-technological challenges and improve the available tools for Emergency Response Teams in indoor and hostile environments without visibility and lack of GNSS signal and communications in fire emergencies (initial stage or advanced). The proposal consists of a sensor network (beacons), \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Image**} & \multicolumn{3}{c}{**Location Estimation for Navigation and Firefighter Tracking**} \\ \cline{3-6}  & \begin{tabular}{c} **Environmental** \\ **Parameters** \\ \end{tabular} & \begin{tabular}{c} **Processing for** \\ **Flame/Fire Focus** \\ **Detection** \\ \end{tabular} & \begin{tabular}{c} **Navigation and** \\ **Tracking** \\ \end{tabular} & \begin{tabular}{c} **Indoor and** \\ **Simulated** \\ \end{tabular} & \
\begin{tabular}{c} **Indoor and** \\ **Scenario** \\ **Evaluation** \\ \end{tabular} &
\begin{tabular}{c} **Modular** \\ **Evaluation** \\ \end{tabular} \\ \hline Our proposal & Yes & Yes & Yes & Yes & Yes & Yes \\ FREAS [11] & No & Yes & No & No & Yes & No \\ \"FireBack\" [9] & Yes & No/Yes & Only See in the map & No & No & No \\ \"A survey\" [12] & No & No & Yes & Yes & Yes & No \\ CROW [14] & No & No & Yes & No & Yes & No \\ ICA K-medoids [16] & No & Yes, but only with & & Only outdoor & Only outdoor & No \\  & & RGB images & No & without smoke & without smoke & No \\ \"Accurate\" [26] & No & No & Yes & No & Yes & No \\ \"Potentialities\" [45] & No & Yes/No & No & No & Yes & No \\ SensorFly [46] & Yes & No/Yes & No & Yes & Yes & No \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of main contributions. a Mobile Ground Autonomous Vehicle (MGAV), and a mobile application (monitoring interface) that has been implemented, deployed, and evaluated. The platform allows offering communications and environmental information from inside to outside. The detection system algorithm can detect the fire points from thermal images with great accuracy (0.73% accuracy and 0.99% precision) in two real scenarios with hot and cold smoke. We will study other intents with fire and the possibility of having a repository of different adjustment parameters for different environments or some self-adjustment strategy. This computer vision algorithm for fire detection has the capability of operating in low visibility conditions. The fire detection algorithm performs a temporal analysis and color filtering to detect candidate fire regions in the image. Afterward, it extracts characteristics that facilitate and allow the discrimination of fire from other similar regions. Moreover, a linear interpolation was used in order to reduce the number of pixels for being processed. We applied Gaussian smoothing, which allows the images to be transformed into the HSV color space to reduce noise. Moreover, the contrast of the image has been improved by combining two techniques CLAHE and ImageEnhace. In the second stage, the candidate regions' segmentation is performed considering that the fire will move from one image to another. In the last stage, the algorithm stores the regions' characteristics classified as fire with their outlines and sends them to the monitoring interface. The combined use of the fire detection system and the environmental sensor network, the first-responder localization, and a set of edge computing algorithms allow improving the firefighter tactics during an emergency intervention and the evacuation from a hostile site. These provide valuable information in an emergency intervention and its impact on the firefighter response times and operational timeframes. For future work related to this paper, considering a more extensive communications range than a star network, a Mesh Network for the robustness of the communications could be proposed. Therefore, the next step would be to evolve to a mesh network topology and have a complete connection between all the nodes.