Abstract
The increasing need for public safety and rapid emergency response has driven the demand for intelligent surveillance systems capable of real-time threat detection. Traditional monitoring methods rely heavily on manual observation, which is inefficient and prone to human error. This project proposes a real-time emergency detection system using image processing techniques to identify fire outbreaks, crowd congestion, and road accidents from live video streams. The system processes video frames using computer vision and deep learning–based object detection models to recognize emergency scenarios accurately. Fire detection is achieved through visual pattern and color analysis, while crowd detection estimates density to identify overcrowding situations. Accident detection focuses on identifying abnormal vehicle behavior and collision patterns. Once an emergency is detected, the system triggers alerts for timely intervention. The proposed solution enhances situational awareness, reduces response time, and improves public safety, making it suitable for smart cities, traffic monitoring, and surveillance applications.
Keywords
Real-Time Emergency Detection · Image Processing · Computer Vision · Fire Detection · Crowd Density Analysis · Accident Detection · Deep Learning · Video Surveillance · Public Safety · Smart City Applications
Citations
IRE Journals:
Mugesh C., Kishore G., Parvathy M. "Real-Time Detection of Fire, Crowd, Accident Using Image Processing" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 135-142 https://doi.org/10.64388/IREV9I10-1715834
IEEE:
Mugesh C., Kishore G., Parvathy M.
"Real-Time Detection of Fire, Crowd, Accident Using Image Processing" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1715834
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