Advertisement

Real-Time Stopped Object Detection by Neural Dual Background Modeling

  • Giorgio Gemignani
  • Lucia Maddalena
  • Alfredo Petrosino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6586)

Abstract

Moving object detection is a relevant step for many computer vision applications, and specifically for real-time color video surveillance systems, where processing time is a challenging issue. We adopt a dual background approach for detecting moving objects and discriminating those that have stopped, based on a neural model capable of learning from past experience and efficiently detecting such objects against scene variations. We propose a GPGPU approach allowing real-time results, by using a mapping of neurons on a 2D flat grid on NVIDIA CUDA. Several experiments show parallel perfomance and how our approach outperforms with respect to OpenMP implementation.

Keywords

Video Surveillance Stopped Object Detection Neural Model GPGPU 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Collins, R.T., et al.: A System for Video Surveillance and Monitoring, The Robotics Institute, Carnegie Mellon University, Tech. Rep. CMU-RI-TR-00-12 (2000)Google Scholar
  2. 2.
    Ferryman, J.M. (ed.): Proceedings of the 9th IEEE International Workshop on PETS, New York, June 18 (2006)Google Scholar
  3. 3.
    Ferryman, J.M. (ed.): Proceedings of the 10th IEEE International Workshop on PETS, Rio de Janeiro, Brazil, October 14 (2007)Google Scholar
  4. 4.
    Herrero-Jaraba, E., et al.: Detected Motion Classification with a Double-background and a Neighborhood-based Difference. Patt. Recogn. Lett. 24, 2079–2092 (2003)CrossRefGoogle Scholar
  5. 5.
    Maddalena, L., Petrosino, A.: A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications. IEEE Transactions on Image Processing 17(7) (July 2008)Google Scholar
  6. 6.
  7. 7.
    Porikli, F., Ivanov, Y., Haga, T.: Robust Abandoned Object Detection Using Dual Foregrounds. EURASIP Journal on Advances in Signal Processing (2008)Google Scholar
  8. 8.
    Proc. of Fourth IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2007). IEEE Computer Society, Los Alamitos (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Giorgio Gemignani
    • 1
  • Lucia Maddalena
    • 2
  • Alfredo Petrosino
    • 1
  1. 1.Centro DirezionaleDSA - University of Naples ParthenopeNaplesItaly
  2. 2.ICAR - National Research CouncilNaplesItaly

Personalised recommendations