2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 13 , Issue 2 , PP: 08-24, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Vikash Kumar Singh 1 * , Durga Sivashankar 2 , Siddharth Sriram 3 , Manish Nagpal 4 , Warish Patel 5 , Shweta Loonkar 6
Doi: https://doi.org/10.54216/JISIoT.130201
This research introduces a novel and comprehensive framework for digital forensics-based crime scene interpretation. The proposed framework comprises five algorithms, each serving a distinct purpose in enhancing image quality, extracting features, matching, and constructing a database, recognizing, and reconstructing objects in 3D, and conducting context-aware analysis. An ablation study validates the necessity of each algorithmic step. The framework consistently outperforms existing methods in terms of accuracy, precision, recall, and processing time. A detailed comparative analysis of parameters further highlights its cost-effectiveness, moderate complexity, superior data integration, and scalability. Visualizations underscore its dominance across multiple metrics and parameters, positioning it as an advanced solution for digital forensic-based object recognition in crime scene interpretation.
Digital forensics , Crime scene interpretation , Object recognition , Preprocessing , Feature extraction , Database construction , 3D reconstruction , Context-aware analysis , Decision support , Performance comparison
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