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Hysteresis Thresholding

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Video Surveillance for Sensor Platforms

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 114))

Abstract

Hysteresis thresholding offers enhanced object detection but is time consuming, requires lots of memory resources, and is unsuitable for VSNs. In this chapter, we present a unified compact architecture that couples Hysteresis Thresholding with connected component analysis and Object Feature Extraction (HT-OFE) in a single pass over the image. Two versions are developed: a high-accuracy pixel-based architecture and a faster block-based one at the expense of some accuracy loss. Unlike queue-based schemes, HT-OFE treats candidate pixels almost as foreground until objects complete; a decision is then made to keep or discard these pixels. Processing on the fly enables faster results and avoids additional passes for handling weak pixels and extracting object features. Moreover, labels are reused so only one compact row is buffered and memory requirements are drastically reduced.

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Authors and Affiliations

  1. University of Louisiana at Lafayette, Lafayette, USA

    Mayssaa Al Najjar & Magdy Bayoumi

  2. Lebanese International University, Beirut, Lebanon

    Milad Ghantous

Authors
  1. Mayssaa Al Najjar
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  2. Milad Ghantous
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  3. Magdy Bayoumi
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Al Najjar, M., Ghantous, M., Bayoumi, M. (2014). Hysteresis Thresholding. In: Video Surveillance for Sensor Platforms. Lecture Notes in Electrical Engineering, vol 114. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1857-3_7

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  • DOI: https://doi.org/10.1007/978-1-4614-1857-3_7

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-1856-6

  • Online ISBN: 978-1-4614-1857-3

  • eBook Packages: EngineeringEngineering (R0)

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