Towards Automatic Bloodstain Pattern Analysis through Cognitive Robots

被引:3
作者
Acampora, Giovanni [1 ]
Vitiello, Autilia [2 ]
Di Nunzio, Ciro [3 ]
Saliva, Maurizio [4 ]
Garofano, Luciano [5 ,6 ]
机构
[1] Nottingham Trent Univ, Sch Sci & Technol, Clifton Lane, Nottingham, England
[2] Univ Salerno, Dept Comp Sci, Fisciano, Italy
[3] Magna Graecia Univ Catanzaro, Catanzaro, Italy
[4] Azienda Sanitaria Locale ASL Napoli 3 Sud, Torre Del Greco, Italy
[5] Arma Carabinieri, Turin, Italy
[6] Italian Acad Forens Sci, Calabria, Italy
来源
2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS | 2015年
关键词
Forensic Intelligence; Bloodstain Pattern Analysis; Cognitive Robotics; Pattern Recognition; Image Processing; Fuzzy Reasoning;
D O I
10.1109/SMC.2015.428
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Bloodstain pattern analysis (BPA) is a forensic discipline that plays a key role in performing a reconstruction of the crime scene related to blood shedding events. By studying the distribution, size and shape of bloodstains, BPA supports worldwide investigation agencies (US FBI, Italian Carabinieri and so on) in identifying the dynamics of a certain act of violence and evaluating the credibility of statements provided by a witness, a victim, or a suspect. However, in spite of its importance, this forensic discipline is still mainly based on manual approaches, making the analysis of a crime scene long, tedious and potentially imperfect. This paper is aimed at presenting a proposal for a robotic framework to automate the BPA in all its steps. In particular, the robotic framework is composed of an Unmanned Aerial Vehicle (UAV) capable of navigating the crime scene, detecting bloodstains, computing the points of origin and preparing a technical report describing the bloody event.
引用
收藏
页码:2447 / 2452
页数:6
相关论文
共 20 条
[1]  
Acampora G., 2014, INT C FUZZ COMP THEO, P211
[2]  
[Anonymous], 2012, SCI FDN CRIME SCENE
[3]  
[Anonymous], 2006, P 5 INT COGN ROB WOR
[4]  
[Anonymous], Pattern Recognition with Fuzzy Objective Function Algorithms,, DOI 10.1007/978-1-4757-0450-1_3
[5]  
Bahamon J. C., 2011, COLLABORATIVE WEB BA
[6]  
Boonkhong K., 2010, Songklanakarin J Sci Technol, V32, P169
[7]  
Cecchetto B. T., 2011, VMV, P369
[8]  
Fish JT., 2013, Crime Scene Investigation
[9]   Direct least square fitting of ellipses [J].
Fitzgibbon, A ;
Pilu, M ;
Fisher, RB .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1999, 21 (05) :476-480
[10]  
Fraser C. S., 2014, ASPRS 2014 ANN C