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An Efficient Solution for People Tracking and Profiling from Video Streams Using Low-Power Compute

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Advances in Computational Collective Intelligence (ICCCI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1287))

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Abstract

Balancing between performance and speed is vital for real-time applications. Given some of the latest edge devices, such as Raspberry Pi 4, Intel Neural Compute Stick 2, or Nvidia Jetson series, edge processing can become a valid choice for deploying computer vision algorithms in real-time scenarios. Object detection and tracking are two common problems that can be solved using such algorithms, which can be deployed with reasonable performance and speed on edge devices. In this paper, we show that the YOLO architecture can be successfully used for object detection and DeepSORT for object tracking on edge devices. The objects of interest in our scenario are persons, thus indicating face detection and tracking as another problem that is solved in the scope of the paper. Using Raspberry Pi 4 and Intel Neural Compute Stick 2, object detection and tracking models can be run on edge devices, though at around half the performance and more than 10 times slower than on a GPU server.

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Acknowledgements

This research was funded by the MARKSENSE project “Real-time Analysis Platform For Persons Flows Based on Artificial Intelligence Algorithms and Intelligent Information Processing for Business and Government Environment”, contract no. 124/13.10.2017, MySMIS 2014 code 119261.

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Authors and Affiliations

  1. University Politehnica of Bucharest, 313 Splaiul Independentei, Bucharest, Romania

    Marius Eduard Cojocea & Traian Rebedea

  2. Open Gov SRL, 95 Blvd. Alexandru Ioan Cuza, Bucharest, Romania

    Marius Eduard Cojocea & Traian Rebedea

Authors
  1. Marius Eduard Cojocea
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  2. Traian Rebedea
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Correspondence to Marius Eduard Cojocea .

Editor information

Editors and Affiliations

  1. Wroclaw University of Economics and Business, Wrocław, Poland

    Marcin Hernes

  2. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  3. University of Newcastle, Newcastle, Australia

    Edward Szczerbicki

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Cojocea, M.E., Rebedea, T. (2020). An Efficient Solution for People Tracking and Profiling from Video Streams Using Low-Power Compute. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_13

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  • DOI: https://doi.org/10.1007/978-3-030-63119-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63118-5

  • Online ISBN: 978-3-030-63119-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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