Information fusion in crime event analysis: A decade survey on data, features and models
Article
Hu, Kaixi, Li, Lin, Tao, Xiaohui, Velasquez, Juan D. and Delaney, Patrick. 2023. "Information fusion in crime event analysis: A decade survey on data, features and models." Information Fusion. 100. https://doi.org/10.1016/j.inffus.2023.101904
Article Title | Information fusion in crime event analysis: A decade survey on data, features and models |
---|---|
ERA Journal ID | 20983 |
Article Category | Article |
Authors | Hu, Kaixi, Li, Lin, Tao, Xiaohui, Velasquez, Juan D. and Delaney, Patrick |
Journal Title | Information Fusion |
Journal Citation | 100 |
Article Number | 101904 |
Number of Pages | 15 |
Year | 2023 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 1566-2535 |
1872-6305 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.inffus.2023.101904 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1566253523002208 |
Abstract | Crime event analysis (CEA) has become increasingly important in assisting humans in preventing future crimes. A fundamental challenge in the research community lies in the dynamics of criminal intents. Offenders’ criminal intents may evolve due to their surroundings, making it difficult for machine learning models to capture them from limited existing information, leading to model uncertainty. As a result, there has been a surge of works exploiting various information related to the evolution of criminal intents, thus enhancing prediction accuracy. This work conducts a comprehensive survey of the past decade (2013–2023) of CEA methods from the perspective of information fusion. We first investigate the categories of crime data and briefly introduce existing CEA tasks as well as evaluation metrics. Then, fusion is systematically reviewed from the bases of multi-modal data, features and machine learning models in terms of different categories of crime data. Finally, we conclude by highlighting some limitations and identifying several future research directions. |
Keywords | Crime event analysis; Dynamics; Criminal intents; Model uncertainty; Information fusion |
ANZSRC Field of Research 2020 | 460206. Knowledge representation and reasoning |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Wuhan University of Technology, China |
School of Mathematics, Physics and Computing | |
Instituto Sistemas Complejos de Ingeniería (ISCI), Chile |
Permalink -
https://research.usq.edu.au/item/z2962/information-fusion-in-crime-event-analysis-a-decade-survey-on-data-features-and-models
101
total views0
total downloads4
views this month0
downloads this month