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Increasing automobile demand has increased traffic hazards, leading to more road accidents. Reducing emergency response time is essential to increase survival probability in road accidents. It remains, however, a challenging task. This paper presents a computer vision based system that automatically detects road accidents from CCTV footage using machine learning and deep learning algorithms. A supervised CNN classifier is used in our project to determine the probability of an accident in every frame. An alert message is displayed on the screen and an email is sent using the SMTP protocol whenever an accident is detected.. This method has been proven to be effective in detecting accidents quickly and accurately. As a result of our computer vision-based system, we can minimize rescue operation delays, saving many lives.
Keywords:
Computer Vision, Deep Learning, Neural Network, CNN classifier, SMTP protocol
Cite Article:
"COMPUTER VISION BASED ACCIDENT DETECTION AND ALERT SYSTEM", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 6, page no.697 - 703, June-2023, Available :http://www.ijrti.org/papers/IJRTI2306106.pdf
Downloads:
000205296
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator