Design and Implementation of an AI-Based 3D Crime Scene Reconstruction from Visual Evidence
Keywords:
3D visualization, Computer vision, Crime scene reconstruction, Object detection, Open3D, YOLOv5Abstract
Artificial Intelligence (AI) and computer vision have significantly improved the performance of object detection systems. However, most existing approaches generate only two-dimensional (2D) outputs, limiting the understanding of spatial relationships between objects. This paper presents a system that integrates deep learning-based object detection with three-dimensional (3D) visualization to address this limitation. The proposed method utilizes the YOLOv5 model for accurate object detection and the Open3D library to construct an interactive 3D environment. Detected objects are mapped onto textured planes and arranged within a 3D space, enabling users to explore the scene from different perspectives. This enhances the interpretation of visual data compared to traditional 2D representations. The system is designed to be simple and efficient, requiring only image input to generate a 3D visualization. Experimental results demonstrate improved understanding and presentation of object detection outcomes. Overall, the proposed approach provides a more intuitive and effective solution for analysing visual data, with potential applications in surveillance, research, and smart monitoring systems.
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