Communication System For Firefighters

Currently firefighters use two-way radios to communicate on the job, and they are forced to write reports based on their memory because there is not an easy way to record the communications between two-way radios. Firefighters need a system to automatically document what happened while they were responding to a call. To save them a significant amount of time when creating reports, our solution is to implement an application that allows firefighters to take pictures, record video and communicate in real time with their team of on-site responders. The proposed system will use a Wireless Local Area Network (WLAN) hosted on the fire truck itself to act as an access point (AP) to which the firefighters can connect. This AP will also save communication between firefighters to a local storage location. Upon return to the fire station, the AP will route all of the information stored locally to a larger database. For now, Wi-Fi will be our communication medium, with a prediction that our technology can eventually be extended to include radio signal

The NASA firefighter's breathing system program

The research is reported in the development of a firefighter's breathing system (FBS) to satisfy the operational requirements of fire departments while remaining within their cost constraints. System definition for the FBS is discussed, and the program status is reported. It is concluded that the most difficult problem in the FBS Program is the achievement of widespread fire department acceptance of the system

Spartan Daily, September 9, 2013

Volume 141, Issue 5https://scholarworks.sjsu.edu/spartandaily/1424/thumbnail.jp

Fireground location understanding by semantic linking of visual objects and building information models

This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding