Gun Identification Using Tensorflow

Mitchell Singleton; Benjamin Taylor; Jacob Taylor; Qingzhong Liu

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