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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM45330

Deep Learning Based Video Surveillance System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM45330

Volume:

9 |

4 |

Apr 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM45330

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SJ

Sujal Jain*1

KM

Krishnakant Mane*2

ARK

Abdul Rehman Khan*3

TK

Tanish Kesarwani*4

PNT

Prof. Navin Trivedi*

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Abstract

This work presents a computer vision and deep learning based real-time vehicle crash detection system. Evaluated for crash event detection from video feeds are four object detection models: YOLOv8, SSD, Faster R-CNN, and EfficientDet. YOLOv8 is chosen as the main model and ideal for live use since it combines speed and accuracy. The system’s connection with Twilio’s WhatsApp API sends automatic alerts and media evidence upon a crash detection, so increasing its relevance for traffic control and public safety. Live video input makes constant surveillance feasible; experimental comparisons help to evaluate every model’s performance. This work presents an intelligent transportation system with scalable solution. Among next improvements are edge deployment, multi-angle video support, and crash intensity classification.

KEYWORDS

Deep Learning Vehicle Crash Detection YOLOv8 SSD Faster R-CNN EfficientDet WhatsApp Alert Twilio API Smart Surveillance Real-time Detection Computer Vision

Cite This Article

Sujal Jain*1, Krishnakant Mane*2, Abdul Rehman Khan*3, Tanish Kesarwani*4, Prof. Navin Trivedi*, "Deep Learning Based Video Surveillance System," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 4, Apr 2025. https://doi.org/10.55041/IJSREM45330

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Publication Date

4/21/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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0

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2

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5

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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