Crowd Analysis and Tracking for Individual Rescue Operation Using Machine Learning

Abstract

Crowd management and rescue operations in densely populated areas pose a significant challenge, particularly in the context of identifying and aiding individuals in distress. By harnessing machine learning, our approach continuously analyzes specific crowd segments, focusing on tracking various hand gestures and their corresponding actions. This data-driven system enables swift gesture recognition and provision of essential support to individuals in need within these defined crowd areas. The paper presents the methodology, experimental results, and future directions for this innovative approach, which holds significant potential for enhancing public safety and optimizing emergency responses in high-density settings