Advertisement

Event Detection Module for Low-Power Camera

Chapter
  • 1.6k Downloads
Part of the KAIST Research Series book series (KAISTRS)

Abstract

In this chapter, we first propose an effective low-power image sensor system for event detection. The system consisting of a low-resolution auxiliary sensor and a high-resolution main sensor operates in two different modes, sleep and wakeup. In the sleep mode, only the auxiliary sensor works for event detection and the main sensor remains off for power saving. In the wake-up mode, the main sensor turns on based on the data sensed by the auxiliary sensor and into normal operation. Second, a new background subtraction algorithm which can be used for event detection is proposed. The algorithm works much faster than conventional algorithms and requires less computation which is critical for low-power operation. In addition, utilization of depth information in background subtraction for 3-D image applications is also presented. Finally, hardware implementation of a low power low-resolution CMOS image sensor (CIS) is presented. The CIS fabricated in 0.18 μm CIS process is designed to be used as an auxiliary sensor in the proposed system. The CIS generates 4-bit image data and consumes only 1.45 mW out of 3 V supply.

Keywords

Low-power image sensor Low-power event detection Background subtraction Depth information CMOS FPGA 

Notes

Acknowledgments

This work is supported by the Center for Integrated Smart Sensors funded by the Ministry of Science, ICT and Future Planning as the Global Frontier Project.

References

  1. 1.
    Tang F (2013) Low-power CMOS image sensor based on column-parallel single-slope/SAR quantization scheme. IEEE Trans Electron Devices 60(8):2561–2566CrossRefGoogle Scholar
  2. 2.
    Fossum ER (1997) CMOS image sensors: electronic camera-on-a-chip. IEEE Trans Electron Devices 44(10):1689–1698Google Scholar
  3. 3.
    Yonemoto K (2000) A CMOS image sensor with a simple fixed-pattern-noise-reduction technology and a hold accumulation diode. IEEE J Solid State Circ 35(12):2038–2043CrossRefGoogle Scholar
  4. 4.
    Cucchiara R, Piccardi M, Prati (2003) A detecting moving objects, ghosts, and shadows in video streams. IEEE Trans Pattern Anal Mach Intell 24:1337–1342Google Scholar
  5. 5.
    Zivkovic Z, van der Heijden F (2006) Efficient adaptive density estimation per image pixel. Patten Recogn Lett 27:773–780CrossRefGoogle Scholar
  6. 6.
    Maddalena L, Petrosino A (2008) A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans Image Process 17:1168–1177MathSciNetCrossRefGoogle Scholar
  7. 7.
    Maddalena L, Petorisino A (2010) A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection. Neural Comput Appl 19:179–186CrossRefGoogle Scholar
  8. 8.
    Yao J, Odobez J-M (2007) Multi-layer background subtraction based on color and texture. In: IEEE conference on computer vision and pattern recognition, June 2007Google Scholar
  9. 9.
    Noh SJ, Jeon M (2012) A new framework for background subtraction using multiple cues. In: The 10th Asian conference on computer visionGoogle Scholar
  10. 10.
    Harville M, Gordon G, Woodfill J (2001) Foreground segmentation using adaptive mixture models in color and depth. In: Proceedings of the IEEE workshop on detection and recognition of events in video, IEEE computer society, Los Alamitos, CA, USA, pp 311Google Scholar
  11. 11.
    Camplani M, Salgado L (2014) Background foreground segmentation with RGB-D Kinect data: an efficient combination of classifiers. J Vis Commun Image Represent 25(1):122136CrossRefGoogle Scholar
  12. 12.
    Fernandez-Sanchez EJ, Rubio L, Diaz J, Ros E (2013) Background subtraction model based on color and depth cues. Mach Vis Appl 25(5):12111225Google Scholar
  13. 13.
    Stauffer C, Grimson W (1999) Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR 1999), pp 246–252Google Scholar
  14. 14.
    Bouwmans T, Baf F El, Vachon B (2008) Background modeling using mixture of gaussians for foreground detection-a survey. Recent Pat Comput Sci 1(3):219–237CrossRefGoogle Scholar
  15. 15.
    Sobral A, Bender L, Parks D, Yao J, Odobez J-M, Noh SJ “A background subtraction library” in GitHub. https://github.com/andrewssobral/bgslibrary
  16. 16.
    David MW (2011) Evaluation: from precision, recall and f-measure to ROC, informedness, markedness and correlation. Int J Mach Learn Technol 2:37–63CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Gwangju Institute of Science and TechnologyGwangju, BukguSouth Korea

Personalised recommendations