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Dress Identification for Camp Security

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Security with Intelligent Computing and Big-data Services (SICBS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 895))

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Abstract

With the development of artificial intelligence and internet of things, the intelligent embedded devices are becoming more and more popular. So it is urgent to achieve the lightweight application of artificial intelligence. In this paper, a dress identification framework is developed for camp security. The framework has both hardware and software. The hardware is composed of LattePanda board, Arduino chip, USB camera, and buzzer. To achieve the identification, we extract the color histogram feature of moving person from the images captured by USB camera, and identify the military dress by using support vector machine algorithm. The equipment can output sound or light signals by Arduino chip, when it identifies a non-military dress. We implement the identification function based on the OpenCV library. The framework can run in real-time, with a reliable precision.

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Author information

Authors and Affiliations

  1. Army Engineering University of PLA, Guanghua Road, Haifu Streat, No. 1, Nanjing, 210007, China

    Jiabao Wang, Yang Li, Yihang Xiong, Zhixuan Zhao & Dexing Kong

Authors
  1. Jiabao Wang
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  2. Yang Li
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  3. Yihang Xiong
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  4. Zhixuan Zhao
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  5. Dexing Kong
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Corresponding author

Correspondence to Jiabao Wang .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Ching-Nung Yang

  2. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Sheng-Lung Peng

  3. University of Canberra, Canberra, ACT, Australia

    Lakhmi C. Jain

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Wang, J., Li, Y., Xiong, Y., Zhao, Z., Kong, D. (2020). Dress Identification for Camp Security. In: Yang, CN., Peng, SL., Jain, L. (eds) Security with Intelligent Computing and Big-data Services. SICBS 2018. Advances in Intelligent Systems and Computing, vol 895. Springer, Cham. https://doi.org/10.1007/978-3-030-16946-6_54

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