A Machine Learning Based Forensic System for Road Accident Detection and Reporting

Abstract

The escalating frequency of road traffic accidents necessitates advanced investigative tools to ensure judicial clarity and rapid insurance processing. Traditional forensic methods often rely on manual video surveillance review and subjective eyewitness accounts, leading to significant delays and potential investigative bias. This research introduces Oracle Forensic v2.0, a machine learning-based forensic system designed to automate the detection and reporting of road accident scenes through an intelligent, end-to-end digital portal. Unlike existing radio-frequency (RF) or sensor-based detection systems that are often sensitive to hardware malfunctions or environmental interference, this vision-based approach leverages high-definition visual data to achieve precise reconstruction. The proposed system utilizes a Flask-based backend coupled with OpenCV for heuristic temporal keyframe extraction, which effectively reduces raw video data into a concise sequence of critical events while maintaining investigative integrity. At the core of the architecture, these extracted frames are processed by the Gemini 3 Flash multimodal AI engine to perform high-level cognitive reasoning, fault allocation, and timeline reconstruction. The model is guided by specialized Forensic Prompt Engineering to identify traffic violations, such as lane departures or signal non-compliance, which are essential for determining legal liability. For data persistence, all structured JSON findings are securely committed to a MongoDB collection, ensuring a permanent and searchable record of every case. The final output is a professionally compiled PDF dossier generated via the ReportLab toolkit, providing law enforcement and insurance agencies with standardized, objective, and courtroom-ready evidence. Experimental results demonstrate that the system drastically reduces investigation time from hours to seconds while maintaining high analytical accuracy across diverse lighting and weather conditions. This work establishes a scalable, zero-hardware solution that strengthens digital forensics in modern smart-city environments.

Introduction

The document presents Oracle Forensic v2.0, an advanced Machine Learning–based digital forensic system designed to automatically reconstruct vehicular accidents from raw video footage. It addresses major challenges in modern traffic investigations, such as delays, human bias, data overload, and lack of structured evidence, which often result in backlogs in insurance and legal processes.

Traditional accident investigations rely heavily on manual video review and subjective interpretation, leading to errors and disputes. Existing AI-based systems mainly focus on accident detection or real-time alerts but do not provide complete forensic reports or automated fault allocation. Additionally, many systems depend on expensive hardware sensors, limiting scalability.

To solve these issues, Oracle Forensic v2.0 introduces a zero-hardware, software-driven approach that allows users to upload raw video files for automated analysis. The system operates in four main stages:

1. Temporal Keyframe Extraction (OpenCV): Reduces video data by over 90% while retaining critical collision frames.

2. Multimodal AI Reasoning (Gemini 3 Flash): Analyzes keyframes to detect traffic violations and determine fault using cognitive reasoning.

3. Data Storage (MongoDB): Stores structured case data permanently for retrieval and documentation.

4. Automated Report Generation (ReportLab): Creates legally formatted forensic PDF dossiers for investigators and insurance authorities.

The system aims to improve accuracy, reduce investigation time, eliminate bias, and ensure scalable deployment across smart cities. Experimental results show that the platform can reconstruct precise accident timelines even in challenging conditions, contributing to the future of digital forensics under the proposed Vision 2036 framework.

Conclusion

The development of Oracle Forensic v2.0 marks a significant advancement in the field of automated accident investigation. By successfully integrating a Flask-based portal with OpenCV and the Gemini 3 Flash multimodal engine, the research has demonstrated that it is possible to transform unstructured video data into actionable legal intelligence with high speed and accuracy. The system effectively addresses the critical research gap of manual investigative bottlenecks and subjective bias in fault allocation. Experimental results confirm that the platform is robust across various environmental conditions and significantly outperforms traditional hardware-dependent systems in terms of scalability and cost-effectiveness. As part of the Vision 2036 framework, this project provides a solid foundation for predictive forensics and intelligent traffic management. Future enhancements will focus on integrating real-time predictive safety analysis and expanding the model to recognize a broader range of complex multi-vehicle collision dynamics.

References

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Copyright

Copyright © 2026 Francis Johan M, Abishek I, Bala Subramanian V, Prof. Sathya M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.