Pre‐set estimation‐based in‐silico silhouette‐based methodology for improving the robustness to viewing direction difference for assisting forensic gait analysis

Author:

Imoto Daisuke1ORCID,Hirabayashi Manato1,Honma Masakatsu1,Kurosawa Kenji2

Affiliation:

1. Artificial Intelligence Section, Second Department of Forensic Science National Research Institute of Police Science Kashiwa Japan

2. Second Department of Forensic Science National Research Institute of Police Science Kashiwa Japan

Abstract

AbstractForensic gait analysis is used to visually and quantitatively analyze information regarding the appearance and style of walking that can be presented as evidence in the court. The demand for analyzing CCTV pedestrian footage in video surveillance has been increasing. The dependence of the accuracy of semiautomatic silhouette‐based analysis, often used in forensic science, on the differences in the viewing directions is a very challenging issue that is yet to be resolved for real case applications. Currently, the different viewing directions used in comparison footage significantly decrease the accuracy of same person analysis when using the silhouette‐based method, often used in the Japanese forensic science domain. A calibration‐based method was previously prosed to resolve this problem, but it requires performing an elaborate measurement procedure at the camera installation site for an accurate analysis. In this study, we propose a novel in‐silico silhouette‐based analysis method that significantly expands the number of viewing direction pre‐set settings to 900 from the 24 used in the previous method. Several software tools have been developed to ensure that all the procedures can be executed on a computer. The experimental results confirm that the accuracy of the proposed method is comparable to that of the calibration‐based method. Furthermore, the practical comparison results from actual consultation confirmed the effectiveness of the proposed method under existing viewing direction differences. We therefore anticipate that the proposed method will be beneficial for improving the analysis accuracy in real cases and therefore serve as a substitute of the previous method.

Funder

Japan Society for the Promotion of Science

Publisher

Wiley

Subject

Genetics,Pathology and Forensic Medicine

Reference44 articles.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Footprints and Controversies;Reference Module in Social Sciences;2024

2. Recurring Gaitset: A Gait Recognition Method Based on Deep Learning and Recurring Layer Transformer;2023 5th International Academic Exchange Conference on Science and Technology Innovation (IAECST);2023-12-08

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