Instinctive Detection of Accident Occurrence using Numerous Machine Learning Techniques with Comparative Study

M Rajamanogaran; G Karthikeyan

doi:10.17485/IJST/v17i17.3152

Article

Instinctive Detection of Accident Occurrence using Numerous Machine Learning Techniques with Comparative Study

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i17.3152

Year: 2024, Volume: 17, Issue: 17, Pages: 1724-1734

Original Article

Instinctive Detection of Accident Occurrence using Numerous Machine Learning Techniques with Comparative Study

M Rajamanogaran¹, G Karthikeyan^2*

¹PG and Research Department of Computer Science, Periyar Government Arts College, Cuddalore, 607001, Tamil Nadu, India
²Assistant Professor, PG and Research Department of Computer Science, Periyar Government Arts College, Cuddalore, 607001, Tamil Nadu, India

*Corresponding Author
Email: [email protected]

Received Date:13 December 2023, Accepted Date:10 March 2024, Published Date:19 April 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: The research work aims to develop an automated detection system that uses video captures to identify roadside accidents or significant events. By alerting nearby hospitals and emergency services, the system reduces response times and potentially saves lives. The system’s integration with existing emergency response systems ensures prompt assistance to those in need. Methods: The video processing pipeline begins by converting video files into frames for analysis and feature extraction. These features serve as inputs for classification algorithms such as Random Forest, SVM, and KNN. The model’s performance is evaluated using a training set and unseen test data, with the predicted classifications compared against the ground truth labels. Findings: Among the tested classification algorithms, the Random Forest algorithm achieved the highest accuracy. Using 128 frames for analysis provided more comprehensive information, yielding a 96% accuracy rate. This combination proves to be a powerful tool in classification tasks, providing reliable and accurate outputs. Novelty: Machine learning algorithms are instrumental in automating accident detection from video captures. They analyse video footage to identify accidents and promptly alert relevant authorities. This technology can also dispatch emergency messages to nearby hospitals, ensuring quick assistance. The consideration of different frame counts in classification improves accuracy by capturing critical moments and patterns. Machine learning algorithms applied in this work significantly enhance emergency response, reduce response times, and potentially save lives.

Keywords:  Emergency Services, Accidents, Feature Extraction, Random Forest, SVM, KNN

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Copyright

© 2024 Rajamanogaran & Karthikeyan. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)