Mobile Fire Evacuation System for Large Public Buildings Based on Artificial Intelligence and IoT

Huixian, Jiang

doi:10.1109/access.2019.2915241

Cited by 68 publications

(29 citation statements)

References 10 publications

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“…The mapping and positioning are based on technologies, such as AutoCAD map, ultra-wide band (UWB), wireless local area network (WLAN), Bluetooth, radio frequency identification (RFID), and ZigBee [18]. However, under fire conditions, the original map may be not applicable due to the collapse of walls or structural components under fire conditions.…”

Section: Description Of the Robot Systemmentioning

confidence: 99%

A Fire Reconnaissance Robot Based on SLAM Position, Thermal Imaging Technologies, and AR Display

Feng

Niu

et al. 2019

Sensors

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Due to hot toxic smoke and unknown risks under fire conditions, detection and relevant reconnaissance are significant in avoiding casualties. A fire reconnaissance robot was therefore developed to assist in the problem by offering important fire information to fire fighters. The robot consists of three main systems, a display operating system, video surveillance, and mapping and positioning navigation. Augmented reality (AR) goggle technology with a display operating system was also developed to free fire fighters’ hands, which enables them to focus on rescuing processes and not system operation. Considering smoke disturbance, a thermal imaging video surveillance system was included to extract information from the complicated fire conditions. Meanwhile, a simultaneous localization and mapping (SLAM) technology was adopted to build the map, together with the help of a mapping and positioning navigation system. This can provide a real-time map under the rapidly changing fire conditions to guide the fire fighters to the fire sources or the trapped occupants. Based on our experiments, it was found that all the tested system components work quite well under the fire conditions, while the video surveillance system produces clear images under dense smoke and a high-temperature environment; SLAM shows a high accuracy with an average error of less than 3.43%; the positioning accuracy error is 0.31 m; and the maximum error for the navigation system is 3.48%. The developed fire reconnaissance robot can provide a practically important platform to improve fire rescue efficiency to reduce the fire casualties of fire fighters.

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Section: Description Of the Robot Systemmentioning

confidence: 99%

A Fire Reconnaissance Robot Based on SLAM Position, Thermal Imaging Technologies, and AR Display

Feng

Niu

et al. 2019

Sensors

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“…The development of new technologies has enabled the design of applications to create decision-making tools to solve the problems of daily life. For example, a mobile pathfinding application finds an optimum path and visualizes it for the user, which facilitates quick and accurate decision-making during an emergency situation [1]. In fact, in designing such an application, it is very important to design easy user interaction using appropriate visualization strategies.…”

Section: Introductionmentioning

confidence: 99%

A Review on Mixed Reality: Current Trends, Challenges and Prospects

2020

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Currently, new technologies have enabled the design of smart applications that are used as decision-making tools in the problems of daily life. The key issue in designing such an application is the increasing level of user interaction. Mixed reality (MR) is an emerging technology that deals with maximum user interaction in the real world compared to other similar technologies. Developing an MR application is complicated, and depends on the different components that have been addressed in previous literature. In addition to the extraction of such components, a comprehensive study that presents a generic framework comprising all components required to develop MR applications needs to be performed. This review studies intensive research to obtain a comprehensive framework for MR applications. The suggested framework comprises five layers: the first layer considers system components; the second and third layers focus on architectural issues for component integration; the fourth layer is the application layer that executes the architecture; and the fifth layer is the user interface layer that enables user interaction. The merits of this study are as follows: this review can act as a proper resource for MR basic concepts, and it introduces MR development steps and analytical models, a simulation toolkit, system types, and architecture types, in addition to practical issues for stakeholders such as considering MR different domains.

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“…Computer video image processing technology lays a solid foundation for the complex analysis, decision-making, and management of mobile devices with intelligent operations to extract reality technology [5]. Since the mobile device has designed a strong video image processing system in the development stage, it has embedded operating structure, touch display, GPS positioning and video recording functions, which provides the basic conditions for the development of the extraction of the reality system to the mobile terminal [6][7]. In the process of using current video image extraction methods to extract video images of mobile terminals, pixels with low probability may be excessively merged due to insufficient lighting, which easily leads to a reduction in the gray level of the video image and loss of detailed information of the video image of the mobile terminal.…”

Section: Introductionmentioning

confidence: 99%

Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

Feng

2020

IEEE Access

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Extracting the fuzzy feature of the mobile video image can effectively improve the low illumination image quality. Traditional methods are used to construct fuzzy feature indexes of mobile terminal video images, and the detailed information of video images is divided, but the bidirectional matching of feature points is ignored, which leads to low extraction accuracy. Therefore, this paper proposes a method for extracting fuzzy features of mobile terminal video images based on SURF-based virtual reality technology. First, perform video image grayscale extraction on the input mobile terminal video image, and detect the closed area in the mobile terminal video image as the radiation invariant area of the terminal video image. Secondly, Hessian matrix is used to detect the feature points of the image, and the non-maximum suppression method and interpolation operation are used to find and locate the extreme value points. Then, the main direction of feature points was determined, and SURF description operator was used for matching to obtain initial matching point pairs. Finally, the obtained fuzzy feature one-way matching result of the video image is matched in two directions, the closest distance ratio is used to match the feature points, and the full constraint condition is used to filter out the wrong matching point pairs, thereby completing the mobile terminal video image fuzzy feature extraction. The experimental results show that the proposed algorithm is effective in feature extraction and matching, stability and speed. The misrecognition rate of the algorithm in this paper is 0.101, and the time used is only 0.41 s, which fully meets the real-time requirements.

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Mobile Fire Evacuation System for Large Public Buildings Based on Artificial Intelligence and IoT

Cited by 68 publications

References 10 publications

A Fire Reconnaissance Robot Based on SLAM Position, Thermal Imaging Technologies, and AR Display

A Fire Reconnaissance Robot Based on SLAM Position, Thermal Imaging Technologies, and AR Display

A Review on Mixed Reality: Current Trends, Challenges and Prospects

Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

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