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Pedestrian Counting Without Tracking for the Security Application of IoT Framework

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Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1252))

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Abstract

Currently, the security of the Internet of Things (IoT) has aroused great concern. Pedestrian counting under video surveillance has become a key problem affecting social security. In this paper we describe a novel and real-time pedestrian counting framework without using any tracking algorithms. Current research under wide overhead cameras are mainly focus on the tracking-based algorithms, however, effective tracking is difficult in most cases. Therefore, we design a line sampling process, based on this strategy, we can achieve a temporal slice image that contains useful head feature information, which can be used for pedestrian counting without the necessity for visual tracking. As is expected, our algorithm is more stable and accurate than existing approaches. In addition, we also design a two-stage detection algorithm, which is used to locate head position. Experimental results indicate that our constructed algorithm can obtain better performance.

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Acknowledgments

This research was partly supported by National Science Foundation, China (No. 61702226), the Natural Science Foundation of Jiangsu Province (Grant no. BK20170200), Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Land and Resources (KF-2018-03-065), the Fundamental Research Funds for the Central Universities (JUSRP11854), China Postdoctoral Science Foundation (2019M661722).

Author information

Authors and Affiliations

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214000, People’s Republic of China

    Tao Zhang & Yan Zhao

  2. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen, China

    Tao Zhang

  3. School of Digital Media, Jiangnan University, Wuxi, 214000, People’s Republic of China

    Mingming Zhang

  4. School of Design, Jiangnan University, Wuxi, 214000, People’s Republic of China

    Bin Yang

Authors
  1. Tao Zhang
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  2. Mingming Zhang
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  3. Bin Yang
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  4. Yan Zhao
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Corresponding author

Correspondence to Tao Zhang .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Jinwei Wang

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Zhang, T., Zhang, M., Yang, B., Zhao, Y. (2020). Pedestrian Counting Without Tracking for the Security Application of IoT Framework. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1252. Springer, Singapore. https://doi.org/10.1007/978-981-15-8083-3_62

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  • DOI: https://doi.org/10.1007/978-981-15-8083-3_62

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8082-6

  • Online ISBN: 978-981-15-8083-3

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