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Auflistung nach Autor:in "Richter, Sabine"

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  • Zeitschriftenartikel
    COMBI: Artificial Intelligence for Computer-Based Forensic Analysis of Persons
    (KI - Künstliche Intelligenz: Vol. 36, No. 2, 2022) Becker, Sven; Heuschkel, Marie; Richter, Sabine; Labudde, Dirk
    During the prosecution process the primary objective is to prove criminal offences to the correct perpetrator to convict them with legal effect. However, in reality this may often be difficult to achieve. Suppose a suspect has been identified and is accused of a bank robbery. Due to the location of the crime, it can be assumed that there is sufficient image and video surveillance footage available, having recorded the perpetrator at the crime scene. Depending on the surveillance system used, there could be even high-resolution material available. In short, optimal conditions seem to be in place for further investigations, especially as far as the identification of the perpetrator and the collection of evidence of their participation in the crime are concerned. However, perpetrators usually act using some kind of concealment to hide their identity. In most cases, they disguise their faces and even their gait. Conventional investigation approaches and methods such as facial recognition and gait analysis then quickly reach their limits. For this reason, an approach based on anthropometric person-specific digital skeletons, so-called rigs, that is being researched by the COMBI research project is presented in this publication. Using these rigs, it should be possible to assign known identities, comparable to suspects, to unknown identities, comparable to perpetrators. The aim of the COMBI research project is to study the anthropometric pattern as a biometric identifier as well as to make it feasible for the standardised application in the taking of evidence by the police and prosecution. The approach is intended to present computer-aided opportunities for the identification of perpetrators that can support already established procedures.
  • Konferenzbeitrag
    The digital skeleton in modern video analysis - inter- and intraspecific comparsionofindividualrigs
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Pistorius, Elena; Richter, Sabine; Labudde, Dirk
    New methods for identifying individuals based on their digital skeleton (rig) gain attention in digital forensics. To ensure a solid comparison, understanding the development of this digital skeleton is crucial. One approach is manually creating a rig using a parametrized 3D room and cameras with embedded pictures, capturing multiple angles of the same person with markers on defined spots. Using these pictures and the room, a movable marker-based rig can be built in software like Blender (https://www.blender.org). It can be used to fit the rigged doll into another scene or compare proportions. For a similar process, algorithm-based software like OpenPose (https://github.com/CMU- Perceptual-Computing-Lab/openpose) estimates a digital skeleton from pictures or videos. The output includes JSON files with joint coordinates and pictures with embedded skeletons, which can manually be processsed into a rig. While these procedure can save time and expand applications, precision and supervision differ from marker-based rigs. A comparative study assessing similarity is discussed in the following paper.
  • Konferenzbeitrag
    Potential approach for targeted matching of people in video footage based on 3D human pose estimation
    (INFORMATIK 2024, 2024) Richter, Sabine; Labudde, Dirk
    Analyzing video footage can make an important contribution to solving crimes, but it is often limited by challenges such as hidden faces. Modern methods of the digital age rely on anthropometric measurements to overcome these limitations. However, current techniques, often originating from other sectors such as the gaming industry, offer new potential. In this context, a possible new approach based on the application of 3D human pose estimation will be presented. This would offer the possibility of directly matching two people (perpetrator, suspect) using one video of each. In particular, this new approach would significantly reduce the scope of the necessary work steps and the time required. This also applies in comparison to methods that already use 2D position estimation. The new approach presented here could contribute to a significant increase in forensic efficiency if successfully implemented after testing and evaluation on a study dataset and real case data.