Publication

Abstract : In recent years, the issue of homelessness has gained significant attention as societies grapple with finding solutions to alleviate the plight of those without stable shelter. To address this pressing concern, this research utilizes advanced video analysis techniques, specifically Vision Transformer, to detect the presence of individuals in homeless populations. By leveraging state-of-the-art technology, the research aims to not only identify individuals but also discern their actions and activities within the monitored environment. The methodology involves the initial detection of persons using Vi Transformer, an innovative deep learning model renowned for its accuracy in object recognition tasks. Once a person is successfully identified, this research further scrutinizes their activities, with a particular focus on detecting instances of conflict or fighting. This step holds immense potential or the safety and well-being of homeless individuals, as it enables the swift notification of nearby authorities or support services in cases of altercation. Crucially, this research delves into the performance metrics of both person detection and activity detection experiments, providing valuable insights into the efficacy of the Vi Transformer model in real-world applications. By assessing the system's accuracy, speed, and reliability, this study contributes to the development of proactive strategies for assisting homeless populations, emphasizing the importance of technology in addressing social challenges.