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Assessing the Impact of Video Compression on Background Subtraction

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Pattern Recognition (ACPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12046))

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Abstract

Video cameras are deployed in large numbers for surveillance. Video is compressed and transmitted over bandwidth limited communication channels. Researches have increasingly focused on automated analysis algorithms to understand video. This raises the question “Does compression have an effect on automated analysis algorithms?”. This is a vital question in understanding the reliability of automated analysis algorithms. In this paper, we evaluated background subtraction algorithm’s performance on videos acquired using a typical surveillance camera under different scenarios. The videos in the dataset are collected from an IP-based surveillance camera and compressed using different bitrates and quantization levels. The experimental results provide insights on the different compression settings and their impact on the expected performance of background subtraction algorithms. Furthermore, we train a classifier that utilizes video quality to predict the performance of an algorithm.

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Acknowledgment

This work was performed in part through the financial assistance award, Multi-tiered Video Analytics for Abnormality Detection and Alerting to Improve Response Time for First Responder Communications and Operations (Grant No. 60NANB17D178), from U.S. Department of Commerce, National Institute of Standards and Technology.

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Authors and Affiliations

  1. University of Houston, 4800 Calhoun Road, Houston, TX, 77204, USA

    Poonam Beniwal, Pranav Mantini & Shishir K. Shah

Authors
  1. Poonam Beniwal
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  2. Pranav Mantini
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  3. Shishir K. Shah
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Corresponding author

Correspondence to Poonam Beniwal .

Editor information

Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Shivakumara Palaiahnakote

  2. Consiglio Nazionale delle Ricerche, ICAR, Naples, Italy

    Gabriella Sanniti di Baja

  3. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  4. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

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Beniwal, P., Mantini, P., Shah, S.K. (2020). Assessing the Impact of Video Compression on Background Subtraction. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_8

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  • DOI: https://doi.org/10.1007/978-3-030-41404-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41403-0

  • Online ISBN: 978-3-030-41404-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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