Crowd Detection Using YOLOv3-Tiny Method and Viola-Jones Algorithm at Mall

Abstract

Indonesia is one of the countries affected by Covid-19 which is spreading quite fast. Lately, the surge in Covid19 cases in Indonesia is quite high, due to the lack of public awareness of the current health protocols, such as avoiding crowds and keeping a distance. The purpose of this study is to reduce crowds that occur in places with a high risk of crowding, for example in mall. Detection is done by using Closed Circuit Television (CCTV) in the mall and using the YOLOv3-Tiny method and the ViolaJones Algorithm to detect the crowd. To support the research, we use the method of literature study and field observation at Cimahi Mall as one of the malls in the area of Bandung Raya. The results show that to reduce the number of crowds that occur in the mall, crowd detection must be carried out using the YOLOv3-Tiny method and the Viola-Jones Algorithm, and a warning system is given if a crowd is detected in the place. The main concept of this system is crowd detection and warning if there is a crowd located on CCTV in the Mall. In our opinion, when this system is running in malls that occur in Indonesia, the number of positive cases of COVID-19 in Indonesia will decrease because there are no crowds. It can be concluded that this system exists as a precaution against the crowds that often occur today at the mall. Prevention is done by detecting crowds and giving warnings if there is a crowd so that positive cases of COVID-19 in Indonesia will be reduced.