US 12,112,262 B2
Real-time prediction program for change in occupants in large exhibition hall based on deep learning
Giltae Song, Busan (KR); and Seonghyeon Kim, Busan (KR)
Assigned to PUSAN NATIONAL UNIVERSITY INDUSTRY—UNIVERSITY COOPERATION FOUNDATION, Busan (KR)
Filed by Pusan National University Industry-University Cooperation Foundation, Busan (KR)
Filed on Nov. 9, 2020, as Appl. No. 17/092,782.
Claims priority of application No. 10-2019-0163500 (KR), filed on Dec. 10, 2019.
Prior Publication US 2021/0174166 A1, Jun. 10, 2021
Int. Cl. G06N 3/08 (2023.01); G06F 18/10 (2023.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06N 3/044 (2023.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06V 20/00 (2022.01); G06V 20/52 (2022.01)
CPC G06N 3/08 (2013.01) [G06F 18/10 (2023.01); G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06N 3/044 (2023.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06V 20/36 (2022.01); G06V 20/52 (2022.01)] 8 Claims
OG exemplary drawing
 
1. A method of predicting a change in the number of occupants within a space in real time using a computer device, the method comprising:
dividing, by at least one processor of the computer device, into zones, a space where a number of occupants is to be predicted and pre-processing collected data related to the number of occupants within the space through simulations;
generating, by the at least one processor, the pre-processed data in a form of time-series data for deep learning;
training, by the at least one processor, a deep learning model for predicting a number of occupants in each of the divided zones using the generated time-series data; and
predicting, by the at least one processor, the number of occupants within the space by inputting, to the trained model, real time data related to the number of occupants within the space collected in real time through socket communication with a server;
wherein the training of the deep learning model comprises:
using an individual model configuring a share prediction model for receiving the time-series data based on an LSTM and outputting one occupancy number in one zone on a specific time; and
training the individual model using the pre-processed data with regard to each of the divided zones, respectively,
wherein a prediction of a share for the space comprising the divided zones is determined as a weighted sum of each share for each of the divided zones on a current time.