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Robust Person Tracking Algorithm Based on Convolutional Neural Network for Indoor Video Surveillance Systems

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Pattern Recognition and Information Processing (PRIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1055))

Abstract

In this paper, we present an algorithm for multi person tracking in indoor surveillance systems based on tracking-by-detection approach. Convolutional Neural Networks (CNNs) for detection and tracking both are used. CNN Yolov3 has been utilized as detector. Person features extraction is performed based on modified CNN ResNet. Proposed architecture includes 29 convolutional and one fully connected layer. Hungarian algorithm is applied for objects association. After that object visibility in the frame is determined based on CNN and color features. For algorithm evaluation prepared videos that was labeled and tested using MOT evaluation metric. The proposed algorithm efficiency is illustrated and confirmed by our experimental results.

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Author information

Authors and Affiliations

  1. Polotsk State University, Blokhina Street, 29, Novopolotsk, Republic of Belarus

    Rykhard Bohush

Authors
  1. Rykhard Bohush
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  2. Iryna Zakharava
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Corresponding author

Correspondence to Rykhard Bohush .

Editor information

Editors and Affiliations

  1. Belarusian State University, Minsk, Belarus

    Sergey V. Ablameyko

  2. Belarusian State University, Minsk, Belarus

    Viktor V. Krasnoproshin

  3. Belarusian State University of Informatics and Radioelectronics, Minsk, Belarus

    Maryna M. Lukashevich

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Bohush, R., Zakharava, I. (2019). Robust Person Tracking Algorithm Based on Convolutional Neural Network for Indoor Video Surveillance Systems. In: Ablameyko, S., Krasnoproshin, V., Lukashevich, M. (eds) Pattern Recognition and Information Processing. PRIP 2019. Communications in Computer and Information Science, vol 1055. Springer, Cham. https://doi.org/10.1007/978-3-030-35430-5_24

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  • DOI: https://doi.org/10.1007/978-3-030-35430-5_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35429-9

  • Online ISBN: 978-3-030-35430-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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