Human Detection to Mitigate Excessive Electrical Energy Usage in Communal Spaces - Bisaria, Aabhas; Chauhan, Devesh; Varshney, Harsh; Kumar, Sushil; Rastogi, Umang; Sharma, Vineet (2024 International Conference on Intelligent Systems for Cybersecurity (ISCS), 2024)
The purpose of this study is to investigate the issue of increased electrical energy consumption in communal settings, with a specific focus on areas such as restrooms and multiplexes as the primary areas of investigation. Not only does the observed behavior increase the financial requirements of end-users and facility operators, but it also places a major strain on energy grids, which in turn leads to a rise in carbon emissions and an intensification of environmental concerns. A unique approach that makes use of the TensorFlow Object Counting API is proposed by the current inquiry as a means of addressing the concern that was highlighted earlier. The purpose of this strategy is to develop a system that can precisely count human subjects in real-time when they are present. To effectively decerase the amount of electricity that is not required, the primary objective of this cutting-edge system is to precisely identify and count the number of people that are present in shared spaces. When it comes to the development of advanced object counting systems, the open-source framework that has been developed on the TensorFlow is a platform that offers a method that is both user-friendly and efficient. Within the realms of item identification, tracking, and counting, the system exhibits capabilities that are both spectacular and resilient. By addressing the dual problems of rising electricity bills and negative effects on the environment, this technology offers a potential solution to the problem. Through this study, an attempt is made to investigate the factors that lead to excessive energy use and to propose a practical solution to this sianificant problem.