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Object Detection

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Video Surveillance for Sensor Platforms

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

Abstract

Object detection is a key step in several computer vision applications. Some of these include 3-D reconstruction, compression, medical imaging, augmented reality, image retrieval, and surveillance applications. It is especially important for visual surveillance sensor nodes as it segments the input image into background and foreground. All following steps analyze only foreground parts of the image; background is discarded. This chapter reviews the main approaches used for surveillance purposes and focuses on background subtraction techniques. It also presents a Hybrid Selective scheme based on Mixture of Gaussian modeling, named HS-MoG. The scheme provides accurate results for outdoor scenes with clutter motion in the background while requiring fewer operations than the original Mixture of Gaussian scheme.

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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

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  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). Object Detection. 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_5

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  • DOI: https://doi.org/10.1007/978-1-4614-1857-3_5

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-1856-6

  • Online ISBN: 978-1-4614-1857-3

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