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Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings

Research Article

Gun Identification Using Tensorflow

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  • @INPROCEEDINGS{10.1007/978-3-030-00557-3_1,
        author={Mitchell Singleton and Benjamin Taylor and Jacob Taylor and Qingzhong Liu},
        title={Gun Identification Using Tensorflow},
        proceedings={Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings},
        proceedings_a={MLICOM},
        year={2018},
        month={10},
        keywords={Tensorflow Gun detection Video surveillance},
        doi={10.1007/978-3-030-00557-3_1}
    }
    
  • Mitchell Singleton
    Benjamin Taylor
    Jacob Taylor
    Qingzhong Liu
    Year: 2018
    Gun Identification Using Tensorflow
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-00557-3_1
Mitchell Singleton1,*, Benjamin Taylor1,*, Jacob Taylor1,*, Qingzhong Liu1,*
  • 1: Sam Houston State University
*Contact email: mitchellsingleton@shsu.edu, benjamin.taylor@shsu.edu, jacobtaylor@shsu.edu, liu@shsu.edu

Abstract

Automatic video surveillance can assist security personnel in the identification of threats. Generally, security personnel are monitoring multiple monitors and a system that would send an alert or warning could give the personnel extra time to scrutinize if a person is carrying a firearm. In this paper, we utilize Google’s Tensorflow API to create a digital framework that will identify handguns in real time video. By utilizing the MobileNetV1 Neural Network algorithm, our system is trained to identify handguns in various orientations, shapes, and sizes, then the intelligent gun identification system will automatically interpret if the subject is carrying a gun or other objects. Our experiments show the efficiency of implemented intelligent gun identification system.

Keywords
Tensorflow Gun detection Video surveillance
Published
2018-10-12
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-00557-3_1
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