Utilizing 3D joints data extracted through depth camera to train classifiers for identifying suicide bomber

作者：

Highlights：

• Identification of a suicide bomber using a 3D depth camera and machine learning techniques.

• Prediction based on real-time 3D posture data of the body joints obtained using a depth camera.

• Observed that 15 to 20 frames are sufficient to identify a suspected suicide bomber.

• Framework is capable to identify a suicide bomber with an average accuracy of 92.30%

摘要

•Identification of a suicide bomber using a 3D depth camera and machine learning techniques.•Prediction based on real-time 3D posture data of the body joints obtained using a depth camera.•Observed that 15 to 20 frames are sufficient to identify a suspected suicide bomber.•Framework is capable to identify a suicide bomber with an average accuracy of 92.30%

论文关键词：Bomber identification,Task analysis,Predictive models,Data models

论文评审过程：Received 4 October 2020, Revised 22 January 2021, Accepted 17 April 2021, Available online 28 April 2021, Version of Record 6 May 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115081