IndShield: Comprehensive Computer Vision System for Real-Time Safety Surveillance

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Suraj R. Sanap; Utkarsh P. Singh; Niranjan N. Patil; Prof. Rahul M. Samant

IndShield: Comprehensive Computer Vision System for Real-Time Safety Surveillance

Authors: Suraj R. Sanap, Utkarsh P. Singh, Niranjan N. Patil, Prof. Rahul M. Samant

Unique Paper ID: 181464
Volume: 12
Issue: 1
PageNo: 5050-5053

Keywords: YOLO MediaPipe Fire Detection PPE Compliance Intrusion Monitoring Pose Estimation Motion Amplification

Abstract:
This paper presents IndShield, an integrated real-time safety surveillance system that leverages advanced computer vision and deep learning techniques to detect and respond to multiple safety hazards simultaneously. The proposed solution encompasses five critical modules: fire detection, personal protective equipment (PPE) compliance verification, restricted zone intrusion monitoring, emergency pose detection, and motion amplification. By employing state-of-the-art models such as YOLO for object detection and MediaPipe for pose estimation, IndShield delivers highaccuracy detection with minimal latency. A Flask-based web interface enables centralized monitoring and management, while the Twilio API ensures real-time SMS alerts to designated personnel. Experimental evaluations demonstrate superior detection accuracy, low false-positive rates, and robust performance under diverse operational conditions. IndShield’s unified architecture significantly enhances situational awareness, making it a practical solution for industrial and public safety environments.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: 1
PageNo: 5050-5053

IndShield: Comprehensive Computer Vision System for Real-Time Safety Surveillance

Available:https://ijirt.org/article?manuscript=181464

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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