Advertisement

A fast DGPSO-motion saliency map based moving object detection

  • Midhula Vijayan
  • Mohan Ramasundaram
Article
  • 102 Downloads

Abstract

The rapid development in the field of computer vision has encouraged researchers to develop vision systems for moving object detection in embedded surveillance applications. The model requires a fast processing algorithm with minimum complexity, which also consumes less memory for computation. This paper proposes a fast moving object detection algorithm to aid foreground segmentation in embedded applications. Dimensionality based Grouping Particle Swarm Optimization-Motion Saliency Map, a variant of the PSO framework combined with saliency map technique is proposed to achieve tighter object detection. The presented technique utilizes the concept of saliency map followed by shadow removal, partial occlusion detection, and Local Difference Pattern based removed object detection. Dimensionality based Grouping Particle Swarm Optimization-Saliency Map of the background model and Dimensionality based Grouping Particle Swarm Optimization-Saliency Map of the incoming frame are used to construct Motion Saliency Map. The proposed model produces a tighter object region compared to the existing naive saliency map based methods. An enhanced texture feature extraction strategy, named as Local Difference Pattern is proposed for removed object detection. This presented moving object detection method is simple and efficient. It consumes less memory for computation. Hence, the algorithm is suitable for embedded surveillance applications. The experimental results show the effectiveness of the proposed method in terms of average processing time in addition to qualitative, and quantitative analyses.

Keywords

Center surround difference Dimensionality based grouping particle swarm optimization Foreground segmentation Local difference pattern Motion saliency map 

Notes

References

  1. 1.
    Allen JG, Xu RY, Jin JS (2004) Object tracking using camshift algorithm and multiple quantized feature spaces. In: Proceedings of the Pan-Sydney area workshop on visual information processing, pp 3–7. Australian Computer Society, IncGoogle Scholar
  2. 2.
    Balcilar M, Sonmez AC (2016) Background estimation method with incremental iterative re-weighted least squares. SIViP 10(1):85–92CrossRefGoogle Scholar
  3. 3.
    Braham M, Van Droogenbroeck M (2016) Deep background subtraction with scene-specific convolutional neural networks. In: 2016 international conference on systems, signals and image processing (IWSSIP), pp 1–4. IEEEGoogle Scholar
  4. 4.
  5. 5.
    Chen Z, Ellis T (2014) A self-adaptive gaussian mixture model. Comput Vis Image Underst 122:35–46CrossRefGoogle Scholar
  6. 6.
    Choudhury SK, Sa PK, Bakshi S, Majhi B (2016) An evaluation of background subtraction for object detection vis-a-vis mitigating challenging scenarios. IEEE Access 4:6133–6150CrossRefGoogle Scholar
  7. 7.
    Dou J, Qin Q, Tu Z (2017) Background subtraction based on circulant matrix. SIViP 11(3):407–414CrossRefGoogle Scholar
  8. 8.
    El Maadi A, Djouadi MS (2015) Using a light dbscan algorithm for visual surveillance of crowded traffic scenes. IETE J Res 61(3):308–320CrossRefGoogle Scholar
  9. 9.
    Elgammal A, Harwood D, Davis L (2000) Non-parametric model for background subtraction. Computer Vision—ECCV 2000:751–767Google Scholar
  10. 10.
    Gao Z, Cheong LF, Wang YX (2014) Block-sparse rpca for salient motion detection. IEEE Trans Pattern Anal Mach Intell 36(10):1975–1987CrossRefGoogle Scholar
  11. 11.
    Gemignani G, Rozza A (2016) A robust approach for the background subtraction based on multi-layered self-organizing maps. IEEE Trans Image Process 25(11):5239–5251MathSciNetCrossRefGoogle Scholar
  12. 12.
    Han XH, Xu G, Chen YW (2013) Robust local ternary patterns for texture categorization. In: 2013 6th international conference on biomedical engineering and informatics (BMEI), pp 846–850. IEEEGoogle Scholar
  13. 13.
    Heikkilä M, Pietikäinen M, Heikkilä J (2004) A texture-based method for detecting moving objects. In: Bmvc, vol 401, pp 1–10Google Scholar
  14. 14.
    Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259CrossRefGoogle Scholar
  15. 15.
    Jodoin JP, Bilodeau GA, Saunier N (2014) Urban tracker: Multiple object tracking in urban mixed traffic. In: 2014 IEEE winter conference on applications of computer vision (WACV), pp 885–892. IEEEGoogle Scholar
  16. 16.
    Lee BY, Liew LH, Cheah WS, Wang YC (2014) Occlusion handling in videos object tracking: A survey. In: IOP conference series: earth and environmental science, vol 18, p 012020. IOP PublishingGoogle Scholar
  17. 17.
    Lee G, Mallipeddi R, Jang GJ, Lee M (2015) A genetic algorithm-based moving object detection for real-time traffic surveillance. IEEE Signal Process Lett 22 (10):1619–1622CrossRefGoogle Scholar
  18. 18.
    Li C, Bao Z, Wang X, Tang J (2018) Moving object detection via robust background modeling with recurring patterns voting. Multimed Tools Appl 77(11):13557–13570.  https://doi.org/10.1007/s11042-017-4975-4 CrossRefGoogle Scholar
  19. 19.
    Lin HH, Chuang JH, Liu TL (2011) Regularized background adaptation: a novel learning rate control scheme for gaussian mixture modeling. IEEE Trans Image Process 20(3):822–836MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Ma YF, Zhang HJ (2002) A model of motion attention for video skimming. In: 2002 international conference on image processing. 2002. Proceedings, vol 1, pp I–I. IEEEGoogle Scholar
  21. 21.
    Maddalena L, Petrosino A (2009) Self organizing and fuzzy modelling for parked vehicles detection. In: International conference on advanced concepts for intelligent vision systems, pp 422–433. SpringerGoogle Scholar
  22. 22.
    Oliver NM, Rosario B, Pentland AP (2000) A bayesian computer vision system for modeling human interactions. IEEE Trans Pattern Anal Mach Intell 22(8):831–843CrossRefGoogle Scholar
  23. 23.
    Pan Z, Liu S, Fu W (2017) A review of visual moving target tracking. Multimed Tools Appl 76(16):16,989–17,018CrossRefGoogle Scholar
  24. 24.
    Paragios N, Deriche R (2000) Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans Pattern Anal Mach Intell 22 (3):266–280CrossRefGoogle Scholar
  25. 25.
    Ramírez-Alonso G, Chacón-Murguía MI (2016) Auto-adaptive parallel som architecture with a modular analysis for dynamic object segmentation in videos. Neurocomputing 175:990–1000CrossRefGoogle Scholar
  26. 26.
    Sajid H, Cheung SCS (2017) Universal multimode background subtraction. IEEE Trans Image Process 26(7):3249–3260MathSciNetCrossRefGoogle Scholar
  27. 27.
    Seidel F, Hage C, Kleinsteuber M (2014) Prost: a smoothed∖ell _p-norm robust online subspace tracking method for background subtraction in video. Mach Vis Appl 25(5):1227–1240CrossRefGoogle Scholar
  28. 28.
    Seo JW, Kim SD (2016) Dynamic background subtraction via sparse representation of dynamic textures in a low-dimensional subspace. SIViP 10(1):29–36CrossRefGoogle Scholar
  29. 29.
    Sheikh Y, Shah M (2005) Bayesian modeling of dynamic scenes for object detection. IEEE Trans Pattern Anal Mach Intell 27(11):1778–1792CrossRefGoogle Scholar
  30. 30.
    Shi Y, et al. (2001) Particle swarm optimization: developments, applications and resources. In: 2001 proceedings of the 2001 congress on evolutionary computation, vol 1, pp 81–86. IEEEGoogle Scholar
  31. 31.
    St-Charles PL, Bilodeau GA, Bergevin R (2015) A self-adjusting approach to change detection based on background word consensus. In: 2015 IEEE winter conference on applications of computer vision (WACV), pp 990–997. IEEEGoogle Scholar
  32. 32.
    St-Charles PL, Bilodeau GA, Bergevin R (2015) Subsense: A universal change detection method with local adaptive sensitivity. IEEE Trans Image Process 24 (1):359–373MathSciNetCrossRefGoogle Scholar
  33. 33.
    St-Charles PL, Bilodeau GA, Bergevin R (2016) Universal background subtraction using word consensus models. IEEE Trans Image Process 25(10):4768–4781MathSciNetCrossRefGoogle Scholar
  34. 34.
    Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: IEEE computer society conference on. computer vision and pattern recognition, 1999, vol 2, pp 246–252. IEEEGoogle Scholar
  35. 35.
    Subudhi BN, Ghosh S, Ghosh A (2012) Object and shadow separation using fuzzy markov random field and local gray level co-occurence matrix based textural features. In: 2012 12th international conference on intelligent systems design and applications (ISDA), pp 95–100. IEEEGoogle Scholar
  36. 36.
    Sun Y, Tao X, Li Y, Lu J (2015) Robust 2d principal component analysis: A structured sparsity regularized approach. IEEE Trans Image Process 24(8):2515–2526MathSciNetCrossRefGoogle Scholar
  37. 37.
    Wang Y, Jodoin PM, Porikli F, Konrad J, Benezeth Y, Ishwar P (2014) Cdnet 2014: an expanded change detection benchmark dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 387–394Google Scholar
  38. 38.
    Woo JW, Lee W, Lee M (2010) A traffic surveillance system using dynamic saliency map and svm boosting. Int J Control Autom Syst 8(5):948–956CrossRefGoogle Scholar
  39. 39.
    Wren CR, Azarbayejani A, Darrell T, Pentland AP (1997) Pfinder: Real-time tracking of the human body. IEEE Trans Pattern Anal Mach Intell 19(7):780–785CrossRefGoogle Scholar
  40. 40.
    Yuan Q, Yin G (2015) Analyzing convergence and rates of convergence of particle swarm optimization algorithms using stochastic approximation methods. IEEE Trans Autom Control 60(7):1760–1773MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Yubing T, Cheikh FA, Guraya FFE, Konik H, Trémeau A (2011) A spatiotemporal saliency model for video surveillance. Cogn Comput 3(1):241–263CrossRefGoogle Scholar
  42. 42.
    Zeng Z, Jia J, Yu D, Chen Y, Zhu Z (2017) Pixel modeling using histograms based on fuzzy partitions for dynamic background subtraction. IEEE Trans Fuzzy Syst 25(3):584–593CrossRefGoogle Scholar
  43. 43.
    Zhao Z, Zhang X, Fang Y (2015) Stacked multilayer self-organizing map for background modeling. IEEE Trans Image Process 24(9):2841–2850MathSciNetCrossRefGoogle Scholar
  44. 44.
    Zhou X, Yang C, Yu W (2013) Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(3):597–610CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology(NIT)TiruchirappalliIndia

Personalised recommendations