Applied Intelligence

, Volume 47, Issue 4, pp 1008–1021 | Cite as

BBBCO and fuzzy entropy based modified background subtraction algorithm for object detection in videos

Article
  • 328 Downloads

Abstract

Background subtraction (BS) is one of the most commonly used methods for detecting moving objects in videos. In this task, moving objectpixels are extracted by subtracting the current frame from a background frame. The obtained difference is compared against a threshold value to classify pixels as belonging to the foreground or background regions. The threshold plays a crucial role in this categorization and can impact the accuracy and preciseness of the object boundaries obtained by the BS algorithm. This paper proposes an approach for enhancing and optimizing the performance of the standard BS algorithm. This approach uses the concept of fuzzy 2-partition entropy and Big Bang–Big Crunch Optimization (BBBCO). BBBCO is a recently proposed evolutionary optimization approach for providing solutions to problems operating on multiple variables within prescribed constraints. BBBCO enhances the standard BS algorithm by framing the problem of parameter detection for BS as an optimization problem, which is solved using the concept of fuzzy 2-partition entropy. The proposed method is evaluated using videos from benchmark datasets and a number of statistical metrics. The method is also compared with standard BS and another recently proposed method. The results show the promise of the proposed method.

Keywords

Videos Background subtraction BBBCO Object detection and tracking Threshold 

References

  1. 1.
    Ling Q, Yan T, Li F, Zhang Y (2014) A background modeling and foreground segmentation approach based on the feedback of moving objects in traffic surveillance systems. Neurocomputing 133(8):32–45CrossRefGoogle Scholar
  2. 2.
    Tian Y, Senior A, Max L (2012) Robust and Efficient Foreground Analysis in Complex Surveillance Videos. Mach Vis Appl 23(5):967–98CrossRefGoogle Scholar
  3. 3.
    Arroyo R, Yebes J, Bergasa LM, Daza IG, Almazán J (2015) Expert Video-Surveillance System for Real-Time Detection of Suspicious Behaviors in Shopping Malls. Expert Syst Appl 42(21):7991–8005CrossRefGoogle Scholar
  4. 4.
    Makris D, Ellis T (2002) Path detection in video surveillance. Image Vis Comput 20(12):895–903CrossRefGoogle Scholar
  5. 5.
    Shakeri M, Zhang H (2012) Real-Time Bird Detection Based on Background Subtraction Proceeding of World Congress on Intelligent Control and Automation at Bejing, China, pp 4507–4510CrossRefGoogle Scholar
  6. 6.
    Heikkila J, Silven O (1999) A Real-Time System for Monitoring of Cyclists and Pedestrians 2 nd IEEE Workshop on Visual Surveillance at Fort Collins, USA, pp 74–81Google Scholar
  7. 7.
    Mandellos NA, Keramitsoglou I, Kiranoudis CT (2011) A background subtraction algorithm for detecting and tracking vehicles. Expert Syst Appl 38(3):1619–1631CrossRefGoogle Scholar
  8. 8.
    Yoshinaga S, Shimada A, Nagahara H, Taniguchi R-I (2014) Object detection based on spatiotemporal background models. Comput Vis Image Underst 122:84–91CrossRefGoogle Scholar
  9. 9.
    Chen Z, Ellis T (2014) A self-adaptive Gaussian mixture model. Comput Vis Image Underst 122:35–46CrossRefGoogle Scholar
  10. 10.
    Spampinato C, Palazzo S, Kavasidis I (2014) A texton-based kernel density estimation approach for background modeling under extreme conditions. Comput Vis Image Underst 122:74–83CrossRefGoogle Scholar
  11. 11.
    Bouwmans T (2014) Traditional and recent approaches in background modeling for foreground detection: an overview, Computer Science Review 11-12, 31–66Google Scholar
  12. 12.
    Shaikh SH, Saeed K, Chaki N (2014) Moving Object Detection Using Background Subtraction Springer Briefs in Computer Science, Springer International Publishing. ISBN: 978-3-319-07385-9, pp 1–6Google Scholar
  13. 13.
    Piccardi M (2004) Background subtraction techniques: a review IEEE international conference on systems, man and cybernetics at the hague, The Netherlands 4, pp 3099–3104Google Scholar
  14. 14.
    Sobral A, Vacavant A (2014) A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput Vis Image Underst 122(6):4–21CrossRefGoogle Scholar
  15. 15.
    Karasulu B, Korukoglu S (2013) Moving object detection and tracking in videos: Performance evaluation software. Springer briefs in computer science, 2013, Springer-Verlag New York, pp. 1–30, ISBN: 978-1-4614-6533-1Google Scholar
  16. 16.
    Li-juan Q, Yue-ting Z, Fei W, Yun-he P (2005) Video segmentation using Maximum Entropy Model. J Zhejiang Univ (Sci) 6(1):47–52Google Scholar
  17. 17.
    Wang F-P, Chungy W-H, Kuo S-Y (2012) An efficient approach to extract moving objects by the h.264 compressed-domain features 12th International Conference on ITS Telecommunications at Taipei, pp 452–456Google Scholar
  18. 18.
    Subudhi BN, Nanda PK, Ghosh A (2011) Entropy based region selection for moving object detection. Pattern Recogn Lett 32:2097–2108CrossRefGoogle Scholar
  19. 19.
    Ma Y-F, Zhang H-J (2001) Detecting Motion object by spatio-temporal Entropy IEEE International Conference on Multimedia and Expo, pp 265–268Google Scholar
  20. 20.
    Karasulu B, Korukoglu S (2012) Moving object detection and tracking by using annealed background subtraction method in videos: Performance optimization. Expert Syst Appl 39:33–43CrossRefGoogle Scholar
  21. 21.
    Erol OK, Eksin I (2006) A new optimization method: Big Bang-Big Crunch. Adv Eng Softw 37:106–111CrossRefGoogle Scholar
  22. 22.
    http://web.itu.edu.tr/okerol/BBBC.html last accessed on April 2016
  23. 23.
    Tang H, Zhou J, Xue S, Xie L (2010) Big bang–big crunch optimization for parameter estimation in structural systems. Mech Syst Signal Process 24(8):2888–2897CrossRefGoogle Scholar
  24. 24.
    Nascimento JC, Marques JS (2006) Performance evaluation of object detection algorithms for video surveillance. IEEE Trans Multimedia 8(4):761–774CrossRefGoogle Scholar
  25. 25.
    Karasulu B, Korukoglu S (2010) A software for performance evaluation and comparison of people detection and tracking methods in video processing. Multimed Tool Appl 11:205–218Google Scholar
  26. 26.
    Kasturi R, Goldgof D, Soundararajan P, Manohar V, Garofolo J, Bowers R et al (2009) Framework for performance evaluation of face, text, and vehicle detection and tracking in video: data, metrics, and protocol. IEEE Trans Pattern Anal Mach Intell 31(2):319–336Google Scholar
  27. 27.
    Lazarevic-McManus N, Renno JR, Makris D, Jones GA (2008) An object-based comparative methodology for motion detection based on the F-measure. Special Issue on Intell Visual Surveillance Understanding 111(1):74–85Google Scholar
  28. 28.
    Cheng HD, Chen JR, Li JG (1998) Threshold selection based on fuzzy c-partition entropy approach. Pattern Recogn 31:857–870CrossRefGoogle Scholar
  29. 29.
    Tang Y, Mu W, Zhang Y, Zhang X (2012) A fast recursive algorithm based on fuzzy 2-partition entropy approach for threshold selection. Neurocomputing 74:3072–3078CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.UIET, Panjab University ChandigarhChandigarhIndia
  2. 2.Department of CSEBBSBEC FatehgarhSahibPunjabIndia

Personalised recommendations