Advertisement

Experimental Study of Intelligent Lighting Control Method for Dark Field Surveillance

  • Haoting LiuEmail author
  • Chang Guo
  • Shuo Yang
  • Weidong Dong
  • Shunliang Pan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 576)

Abstract

A kind of intelligent lighting control method for the dark field surveillance is proposed. The design of it includes an offline stage and an online stage. Regarding the offline stage, first a visual ergonomic experiment is used to accumulate image datasets which have different subjective image quality evaluation degrees (IQEDs) for the typical surveillance application. Second, the objective IQED metrics are computed for these datasets above: the image region contrast, the image edge blur, the image gray deviation, and the image noise. Third, the k-means cluster method is employed to analyze the distribution thresholds of the objective metrics. Regarding the online stage, first the objective IQED metrics are computed for the input image. Then, the computed objective results will be compared with the distribution thresholds gotten in the offline stage. Finally, a kind of optimal lighting control will be performed. A lighting experimental system is built, and many experimental results have verified the correctness of the proposed method.

Keywords

Intelligent lighting Visual ergonomics Dark field surveillance Image quality Feedback control 

Notes

Acknowledgements

This work is supported by the National Nature Science Foundation of China under Grant No. 61501016 and the open project of the State Key Laboratory of Intense Pulsed Radiation Simulation and Effect under Grant No. SKLIPR1713.

Compliance with Ethical Standards

The study was approved by the Logistics Department for Civilian Ethics Committee of University of Science and Technology Beijing. All subjects who participated in the experiment were provided with and signed an informed consent form. All relevant ethical safeguards have been met with regard to subject protection.

References

  1. 1.
    Nazare AC Jr, Schwartz WR (2016) A scalable and flexible framework for smart video surveillance. Comput Vis Image Und 144:258–275CrossRefGoogle Scholar
  2. 2.
    Mabrouk AB, Zagrouba E (2018) Abnormal behavior recognition for intelligent video surveillance systems: a review. Expert Syst Appl 91:480–491CrossRefGoogle Scholar
  3. 3.
    Hassan Y, Orabi M, Ismeil M et al (2017) Study the effect of series and parallel LEDs connections on the output current ripple for LED driver of solar street lighting. In: IMESC, pp 1492–1499Google Scholar
  4. 4.
    Ran L, Zhang X, Zhao C et al (2013) Preliminary exploration on visual ergonomics in LED illumination and national standard system framework for visual ergonomics. In: CIFSSL, pp 198–201Google Scholar
  5. 5.
    Liu H, Lu H, Zhang Y (2017) Image enhancement for outdoor long-range surveillance using IQ-learning multiscale Retinex. IET Image Process 11:786–795CrossRefGoogle Scholar
  6. 6.
    Lee H, Jung S, Kim M et al (2017) Synthetic minority over-sampling technique based on fuzzy K-means clustering for imbalanced data. In: ICFTA, pp 1–6Google Scholar
  7. 7.
    Liu H, Zhou Q, Yang J et al (2017) Intelligent luminance control of lighting systems based on imaging sensor feedback. Sensors 17: 321-1–321-24CrossRefGoogle Scholar
  8. 8.
    Saxena A, Prasad M, Gupta A et al (2017) A review of clustering techniques and developments. Neurocomputing 267:664–681CrossRefGoogle Scholar
  9. 9.
    Liu H, Yan B, Lv M et al (2017) Computer-aided visual function assessment using subjective image quality evaluation metrics. In: ICMMESE, pp 57–65Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Haoting Liu
    • 1
    Email author
  • Chang Guo
    • 1
  • Shuo Yang
    • 2
  • Weidong Dong
    • 2
  • Shunliang Pan
    • 2
  1. 1.School of Automation and Electrical Engineering, Beijing Engineering Research Center of Industrial Spectrum ImagingUniversity of Science and Technology BeijingBeijingChina
  2. 2.Institute of Manned Space System EngineeringBeijingChina

Personalised recommendations