The documentation of outdoor forensic crime scenes involving human remains poses multiple challenges to law enforcement officials documenting the context of the scene. More rigorous protocols for the documentation of these scenes are needed, and recent advances in technology open up the possibility of three-dimensional (3D) documentation of the scene. More commonly used in archaeological contexts, close-range photogrammetry (CRP) creates reliable 3D models through triangulation of overlapping points between multiple photographs. This documentation technique allows for the preservation of the entire 3D context and the virtual manipulation without evidence destruction. However, CRP has only been preliminarily used in the field of forensic archaeology. To further refine data collection and processing protocols for the use of CRP on forensic scenes, four mock crime scenes on different complex ground surfaces involving a surface scatter of human remains were created. Photographs were collected using a Nikon D7200 camera and processed using Agisoft Metashape Professional. The first scene, a mixed surface dominated by leaves, was used to test the number of images that should be incorporated into a 3D scene. The second (mixed surface dominated by grass), third (mixed surface dominated by pine needles), and fourth (ground surface of mixed leaves and dirt) scenes were used to test data collection techniques, the capability of documenting a scene using CRP on different complex surfaces, as well as to illustrate complex variables that may affect the final model. Accuracy was determined through an estimation of the final root mean square error (RMSE) and visual analysis of qualitative errors. Results show that CRP can be used to accurately and reliably document the 3D context of real-life scenes in a time-efficient and cost-effective manner. This new methodology should be integrated into current forensic crime scene documentation and may eventually replace current methods of mapping the scene context.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1253 |
| Date | 01 January 2020 |
| Creators | Mccollum, Megan |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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