Crowd Management Surveillance using Artificial Intelligence and Deep Learning
Authors: Vaibhav Bilade, Chetan Fulaware, Ankita Gaikwad, A.G. Sayyad
Abstract: In today’s rapidly evolving technological landscape, the integration of artificial intelligence (AI) in video surveillance systems has emerged as a paramount solution to address diverse safety and security challenges. This paper presents an AI-powered video surveillance system that leverages advanced computer vision techniques to enhance situational awareness in real-time video streams, both from recorded video input and live web cameras. The system incorporates the following key features: 1. Fall Detection: The system utilizes AI algorithms to detect and promptly respond to incidents of individuals falling within the surveillance area. By identifying such events, the system ensures rapid assistance, especially for vulnerable populations, thereby mitigating potential harm and reducing emergency response time. 2. Overcrowd Detection: Overcrowding in public spaces is a common safety concern. Our system employs AI to analyze video feeds and identify instances of overcrowding. This enables authorities to take proactive measures to manage crowd density, maintain public safety, and prevent potential emergencies. 3. Vehicle Crash Detection: Automated vehicle crash detection is vital for traffic management and immediate response to accidents. The system employs AI algorithms to detect vehicle crashes by analyzing video feeds from roads and highways. This feature not only helps in efficient traffic management but also expedites emergency services to accident sites, potentially saving lives. 4. Fire and Weapon Detection: Early detection of fires and weapons is crucial for public safety and security. The AI-powered system is designed to identify instances of fires and the presence of weapons within the surveillance area. This capability allows for rapid response to fire emergencies and potential threats, ultimately safeguarding lives and property. The system supports real-time video analysis from both recorded video input and live web cameras, making it versatile and adaptable for a wide range of applications
Paper Id: 230392
Published On: 2023-11-21
Published In: Volume 11, Issue 6, November-December 2023
Cite This: Crowd Management Surveillance using Artificial Intelligence and Deep Learning - Vaibhav Bilade, Chetan Fulaware, Ankita Gaikwad, A.G. Sayyad - IJIRMPS Volume 11, Issue 6, November-December 2023.