• Journal of Industrial Convergence
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• v.21 no.4
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• pp.165-170
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• 2023

There are cases where the mobility impaired have difficulty moving from the moment they leave the house. If guardians also do not have time to entrust their families, who are socially disadvantaged, to a shelter, the guardian has no choice but to check directly in order to know the location of the guardian. The AI home care solution was designed to relieve the anxiety and labor of caregivers and to provide convenience for protection facility officials and users. If more facilities distribute and use services free of charge to non-profit foundations and protective facilities, the concern of guardians will be reduced, and the burden of facility officials who have to manage facility users will be reduced. In this paper, we provide emergency notification services to guardians in the event of an emergency as well as location and status alarms for guardians, which are all data related to movement, in consideration of the mobility vulnerable. Furthermore, it is necessary to provide a service function that recommends the optimal route using a navigation function to ease the convenience and burden of facility officials. It is necessary to alleviate anxiety by providing necessary information to the guardian, such as the location of the shuttle used by the mobile weak and the time of getting on and off. In addition, while providing services for free, the goal is to improve the quality of service for facility managers and the quality of service for the mobility weak.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.667-674
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• 2023

In the event of a disaster occurring within a building, the prompt and efficient evacuation and rescue of occupants within the building becomes the foremost priority to minimize casualties. For the purpose of such rescue operations, it is essential to ascertain the distribution of individuals within the building. Nevertheless, there is a primary dependence on accounts provided by pertinent individuals like building proprietors or security staff, alongside fundamental data encompassing floor dimensions and maximum capacity. Consequently, accurate determination of the number of occupants within the building holds paramount significance in reducing uncertainties at the site and facilitating effective rescue activities during the golden hour. This research introduces a methodology employing computer vision algorithms to count the number of occupants within distinct building locations based on images captured by installed multiple CCTV cameras. The counting methodology consists of three stages: (1) establishing virtual Lines of Interest (LOI) for each camera to construct a multi-camera network environment, (2) detecting and tracking people within the monitoring area using deep learning, and (3) aggregating counts across the multi-camera network. The proposed methodology was validated through experiments conducted in a five-story building with the average accurary of 89.9% and the average MAE of 0.178 and RMSE of 0.339, and the advantages of using multiple cameras for occupant counting were explained. This paper showed the potential of the proposed methodology for more effective and timely disaster management through common surveillance systems by providing prompt occupancy information.