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Self-organized night video enhancement for surveillance systems

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Abstract

Night video enhancement techniques are widely used for identifying suspicious activities captured by night visual surveillance systems. However, artificial light sources present in the surroundings deteriorate the visual quality of the video captured during night. This non-uniform illumination reduces the object identification and tracking capability of a real-time visual security system. Thus, a uniform enhancement technique is insufficient for handling such uneven illumination. In this paper, we propose a novel night video enhancement scheme based on a hierarchical self-organizing network. This proposed scheme automatically groups and enhances the neighboring pixels of dark and light regions in each frame. In this scheme, two-level self- organizing neural networks were hierarchically arranged to group similar pixels present in the night video frame. We applied the no-reference-based performance evaluation metrics for measuring the objective quality of the video. The experimental results showed that our proposed approach considerably enhances the visual perception of the video captured at night under varied illumination conditions.

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Notes

  1. http://www.taoyangjingli.net/data,5/11/2007.

  2. http://r0k.us/graphics/kodak/kodim07.html.

  3. http://www.cs.tut.fi/~foi/GCF-BM3D.

  4. http://live.ece.utexas.edu/,06/02/2015.

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Acknowledgments

The authors would like to thank the Centre for Engineering Research and Development, Government of Kerala, for research fellowship and Tao Yang for sharing databases.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, College of Engineering Trivandrum, LBS Centre for Science and Technology, Thiruvananthapuram, Kerala, India

    T. Soumya

  2. Indian Institute of Information Technology and Management-Kerala (IIITM-K), Thiruvananthapuram, Kerala, India

    Sabu M. Thampi

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  1. T. Soumya
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Correspondence to T. Soumya.

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Soumya, T., Thampi, S.M. Self-organized night video enhancement for surveillance systems. SIViP 11, 57–64 (2017). https://doi.org/10.1007/s11760-016-0893-6

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