AN ULTRASONIC SENSOR FOR HUMAN PRESENCE DETECTION TO ASSIST RESCUE WORK IN LARGE BUILDINGS

When the fire brigade arrives at a burning building, it is of vital importance that people who are still inside can quickly be found. Smart buildings should be able to expose this location data to the fire brigade working in a smart city. In this paper the feasibility is researched of using ultrasonic sound sensors for human presence detection in smoke-filled spaces. This type of sensor could assist the fire brigade when evacuating a large building by directing them to the places where their help is most needed. The advantage of ultrasonic sound over other sensors or cameras is that its signal is able to pierce through smoke, does not require badges or other wearable devices and introduces little privacy and security risks. In addition, ultrasonic sensors are very inexpensive making it possible to equip every room of a building with an ultrasonic presence detector. In this research both a preliminary ultrasound measuring device and signal processing algorithm have been designed. Testing results show that the walking movement of a person in an indoor area can be detected with the combination of the sensor and the algorithms. In addition, tests of the signal strength in smoke have shown that ultrasound is capable of “looking through” the smoke. The algorithm based on a particle filter allows for more information to be extracted from the relatively simple sensor signal by detecting human walking movement specifically and opens up the way for an ultrasound based indoor positioning system that can be used in emergency situations

NEEDLE IN A HAYSTACK: FEASIBILITY OF IDENTIFYING SMALL SAFETY ASSETS FROM POINT CLOUDS USING DEEP LEARNING

Asset management systems are beneficial for maintaining building infrastructure and can be used to keep up-to-date records of relevant safety assets, such as smoke detectors, exit signs, and fire extinguishers. Existing methods for locating and identifying these assets in buildings primarily rely on surveys and images, which only provide 2D locations and can be tedious for large-scale structures. Indoor point clouds, which can be captured quickly for buildings using mobile scanning techniques, can provide us with 3D asset locations. In this paper, we study the feasibility of using 3D point clouds of buildings combined with deep learning techniques to identify safety-related assets, particularly small-sized assets like fire switches and exit signs. We adopt the state-of-the-art Deep Learning network, Kernel Point-Fully Convolutional Network (KP-FCNN), to identify these assets through semantic segmentation. Using the obtained results, we create a 3D-geometry model of the building with assets pinpointed, providing scene semantics and delivering more value. Our method is tested using three different point cloud datasets obtained from a depth camera, a mobile laser scanner, and an iPhone lidar sensor

An Ultrasonic Sensor for Human Presence Detection to Assist Rescue Work in Large Buildings

When the fire brigade arrives at a burning building, it is of vital importance that people who are still inside can quickly be found. Smart buildings should be able to expose this location data to the fire brigade working in a smart city. In this paper the feasibility is researched of using ultrasonic sound sensors for human presence detection in smoke-filled spaces. This type of sensor could assist the fire brigade when evacuating a large building by directing them to the places where their help is most needed. The advantage of ultrasonic sound over other sensors or cameras is that its signal is able to pierce through smoke, does not require badges or other wearable devices and introduces little privacy and security risks. In addition, ultrasonic sensors are very inexpensive making it possible to equip every room of a building with an ultrasonic presence detector. In this research both a preliminary ultrasound measuring device and signal processing algorithm have been designed. Testing results show that the walking movement of a person in an indoor area can be detected with the combination of the sensor and the algorithms. In addition, tests of the signal strength in smoke have shown that ultrasound is capable of “looking through” the smoke. The algorithm based on a particle filter allows for more information to be extracted from the relatively simple sensor signal by detecting human walking movement specifically and opens up the way for an ultrasound based indoor positioning system that can be used in emergency situations.Microwave Sensing, Signals & SystemsStatisticsOLD Department of GIS TechnologyOLD Urban Desig