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Title [sv]
AI-driven brottsplatsanalys
Title [en]
AI-Powered Crime Scene Analysis
Abstract [sv]
Syfte och mål:Projektet handlar om automatisk analys av brottsplatser inomhus. Det ska studera AI-teknologier för miljökartläggning, segmentering och klassificering av objekt och spår, bl.a. för att undvika kontaminering av bevis och varna för farliga situationer.Förväntade effekter och resultat:Brottsplatsundersökning görs av kriminaltekniska experter. Projektet ska bidra med automatisering av rutinuppgifter vid ankomst. Resultat ska erbjuda effektiv användning av tid vid ankomst till platsen. Resultatet ska också erbjudas som en icke kontaminerad modell av platsen, vilket bidrar till bättre analys under alla steg och faser.Upplägg och genomförande:För att uppnå målen ska datorseende genom synliga, infraröda, termiska sensorer så väl som icke-foto sensorer, som djupdata erhållna av LIDAR utforskas. För att reducera kontaminering ska nanodroner studeras. Detta är i sig en utmaning, eftersom drönare med lämpliga sensorer är för stora, olämpliga t.ex. i små lägenheter. Vi ska därför undersöka även användningen av smartphones eller kroppskameror av första-respondenter.
Abstract [en]
Purpose and goal:The project is about automatic analysis of indoor crime scenes. We will study AI technologies for environment mapping, segmentation and classification of objects and traces found at such scenarios worthy of immediate investigation, eg to avoid contamination of the scene or warn for hazardous situations.Expected results and effects:Crime scene investigation is normally done by forensics experts upon arrival. The present project will automatize these tasks, allowing the team to directly concentrate on the analysis of important cues, thus saving precious time during the first moments after a crime. Outputs will also remain as an uncontaminated model of the scene, allowing post-analysis, if necessary, during any step of the investigation.Approach and implementation:To achieve our aims, we will explore vision technologies like visible, IR, thermal, and non-vision depth sensors like LIDAR. To ensure that the scene is contaminated to the least possible extent, we will investigate the use of nanodrones. This is a challenge, since existing drones equipped with those sensors are usually big and unsuitable, for example, for small flats. To counteract potential difficulties in such innovative task, we will also investigate the use of smartphones or bodycams worn by first-responders.
Publications (1 of 1) Show all publications
Cooney, M., Ponrajan, S. & Alonso-Fernandez, F. (2025). Nano Drone-based Indoor Crime Scene Analysis*. In: 2025 IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO): . Paper presented at 2025 IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO), Osaka, Japan, 17-19 July, 2025 (pp. 20-27). Piscataway, NJ: IEEE
Open this publication in new window or tab >>Nano Drone-based Indoor Crime Scene Analysis*
2025 (English)In: 2025 IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO), Piscataway, NJ: IEEE, 2025, p. 20-27Conference paper, Published paper (Refereed)
Abstract [en]

Technologies such as robotics, Artificial Intelligence (AI), and Computer Vision (CV) can be applied to crime scene analysis (CSA) to help protect lives, facilitate justice, and deter crime, but an overview of the tasks that can be automated has been lacking. Here we follow a speculative prototyping approach: First, the STAIR tool is used to rapidly review the literature and identify tasks that seem to have not received much attention, like accessing crime scenes through a window, mapping/gathering evidence, and analyzing blood smears. Secondly, we present a prototype of a small drone that implements these three tasks with 75%,85%, and 80% performance, to perform a minimal analysis of an indoor crime scene. Lessons learned are reported, toward guiding next work. ©2025 IEEE

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2025
Series
IEEE Workshop on Advanced Robotics and its Social Impacts. Conference Proceedings, ISSN 2162-7576
Keywords
Computer vision, Image analysis, Reviews, Prototypes, Stairs, Artificial intelligence, Blood, Drones
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-58459 (URN)10.1109/ARSO64737.2025.11124976 (DOI)979-8-3315-1101-2 (ISBN)
Conference
2025 IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO), Osaka, Japan, 17-19 July, 2025
Funder
Vinnova, 2022-00919
Available from: 2026-02-17 Created: 2026-02-17 Last updated: 2026-02-18Bibliographically approved
Principal InvestigatorAlonso-Fernandez, Fernando
Coordinating organisation
Halmstad University
Funder
Period
2022-10-31 - 2025-10-30
National Category
Signal Processing
Identifiers
DiVA, id: project:2983Project, id: 2022-00919_Vinnova

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