Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1–8, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-1-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1–8, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-1-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

PANORAMIC IMAGES, 2D FEATURE-BASED AND CHANGE DETECTION METHODS FOR THE DOCUMENTATION OF CONTAMINATED CRIME SCENES

D. Abate1,2, I. Toschi3, C. Sturdy-Colls1, and F. Remondino3 D. Abate et al.
  • 1Centre of Archaeology, Staffordshire University, Stoke on Trent, United Kingdom
  • 2Science and Technology in Archaeology research Centre, STARC, The Cyprus Institute, Nicosia, Cyprus
  • 33D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy

Keywords: Feature Extractors, SIFT, Change Detection, MAD, 360 cameras, Crime Scene Documentation, Photogrammetry

Abstract. This paper aims to propose and validate a methodology which can support forensic technicians while documenting a crime scene, after a contamination event (either accidental or deliberate) has changed its original appearance. Indeed, investigators need fast and automated tools to detect changes that occurred at a scene over time, and solutions to this problem are still an open issue. The contribution of the paper for addressing this need is twofold. First, a new methodology is introduced, that exploits panoramic images acquired with the Ricoh Theta SC camera, and 2D feature-based methods. The core idea is that SIFT features inherently contain scene information and, thanks to their good stability and invariance, can be exploited to detect possible changes that occurred at a scene surveyed with multi-temporal images. Second, in order to evaluate the performance of the proposed methodology, a reference approach is applied, based on state-of-the-art change detection algorithms (MAF/MAD), originally developed for remote sensing applications. Both methods are tested by simulating a typical crime scene, and a contamination event at the Crime Scene House (UK).