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A self-organizing map to improve vehicle detection in flow monitoring systems

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Abstract

The obtaining of perfect foreground segmentation masks still remains as a challenging task in video surveillance systems, since errors in that initial stage could lead to misleadings in subsequent tasks as object tracking and behavior analysis. This work presents a novel methodology based on self-organizing neural networks and Gaussian distributions to detect unusual objects in the scene, and to improve the foreground mask handling occlusions between objects. After testing the proposed approach on several traffic sequences obtained from public repositories, the results demonstrate that this methodology is promising and suitable to correct segmentation errors on crowded scenes with rigid objects.

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Notes

  1. Datasets of NGSIM are available at http://ngsim-community.org/.

References

  • Álvarez S, Llorca D, Sotelo M (2014) Hierarchical camera auto-calibration for traffic surveillance systems. Expert Syst Appl 41(4, Part 1):1532–1542

    Article  Google Scholar 

  • Cancela B, Ortega M, Fernández A, Penedo MG (2013) Hierarchical framework for robust and fast multiple-target tracking in surveillance scenarios. Expert Syst Appl 40(4):1116–1131

    Article  Google Scholar 

  • Cao X, Suganthan P (2003) Video shot motion characterization based on hierarchical overlapped growing neural gas networks. Multimedia Syst 9:378–385

    Article  Google Scholar 

  • Cappé O, Douc R, Guillin A, Marin JM, Robert CP (2008) Adaptive importance sampling in general mixture classes. Stat Comput 18:447–459

    Article  MathSciNet  Google Scholar 

  • Cinque L, Foresti GL, Lombardi L (1999) Multiresolution approach for image coding and transmission for traffic scene monitoring via the web. Proc SPIE 3964:344–349

    Article  Google Scholar 

  • Cucchiara R, Piccardi M, Mello P (2000) Image analysis and rule-based reasoning for a traffic monitoring system. Intell Transp Syst IEEE Trans 1(2):119–130

    Article  Google Scholar 

  • de Angulo V, Torras C (2008) Learning inverse kinematics: reduced sampling through decomposition into virtual robots. IEEE Trans Syst Man Cybern Part B: Cybern 38(6):1571–1577

    Article  Google Scholar 

  • Diaz-Huerta CC, Felipe-Riveron EM, Montaño Zetina LM (2014) Quantitative analysis of morphological techniques for automatic classification of micro-calcifications in digitized mammograms. Expert Syst Appl 41(16):7361–7369

    Article  Google Scholar 

  • Fabrizio J, Marcotegui B, Cord M (2013) Text detection in street level images. Pattern Anal Appl 16(4):519–533

    Article  MathSciNet  Google Scholar 

  • Fathy M, Siyal MY (1995) An image detection technique based on morphological edge detection and background differencing for real-time traffic analysis. Pattern Recognit Lett 16(12):1321–1330

    Article  Google Scholar 

  • Flórez F, García J, García J, Hernández A (2002) Hand gesture recognition following the dynamics of a topology-preserving network. In: Proceedings of the 5th IEEE international conference on automatic face and gesture recognition, pp 318–323

  • Frezza-Buet H (2008) Following non-stationary distributions by controlling the vector quantization accuracy of a growing neural gas network. Neurocomputing 71:1191–1202

    Article  Google Scholar 

  • Fritzke B (1995) A growing neural gas network learns topologies. Adv Neural Inf Process Syst 7:625–632

  • Fritzke B (1997) A self-organizing network that can follow non-stationary distributions. In: Proceedings of the international conference on artificial neural networks. Springer, pp 613–618

  • Gangopadhyay A, Chatterjee O, Chatterjee A (2013) Hand shape based biometric authentication system using radon transform and collaborative representation based classification. In: IEEE 2nd international conference on image information processing (ICIIP), pp 635–639

  • García-Rodríguez J, Domínguez E, Angelopoulou A, García-chamizo JM (2011) Video and image processing with self-organizing. Lect Notes Comput Sci 6692:98–104

    Article  Google Scholar 

  • Gonzalez-Castro V, Debayle J, Pinoli JC (2014) Color adaptive neighborhood mathematical morphology and its application to pixel-level classification. Pattern Recognit Lett 47(0):50–62

  • Göppert J, Rosenstiel W (1997) The continuous interpolating self-organizing map. Neural Process Lett 5(3):185–192

    Article  Google Scholar 

  • Gritsch G, Donath N, Litzenberger M (2009) Night-time vehicle classification with an embedded, vision system. In: Proceedings of the IEEE international conference on intelligent transportation systems, pp 1–6

  • Hoogerheide L, Opschoor A, van Dijk HK (2012) A class of adaptive importance sampling weighted EM algorithms for efficient and robust posterior and predictive simulation. J Econom 171:101–120

    Article  Google Scholar 

  • Kastrinaki V, Zervakis M, Kalaitzakis K (2003) A survey of video processing techniques for traffic applications. Image Vis Comput 21(4):359–381

    Article  Google Scholar 

  • Kohonen T (1982a) Clustering, taxonomy, and topological maps of patterns. In: Proceedings of the 6th international conference on pattern recognition, pp 114–128, IEEE

  • Kohonen T (1982b) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69

    Article  MathSciNet  MATH  Google Scholar 

  • Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480

    Article  Google Scholar 

  • Kohonen T, Schroeder MR, Huang TS (eds) (2001) Self-organizing maps, 3rd edn. Springer, New York

    MATH  Google Scholar 

  • Kohonen T (2013) Essentials of the self-organizing map. Neural Netw 37(0):52–65 Twenty-fifth anniversay commemorative issue

    Article  Google Scholar 

  • Lai J, Huang S, Tseng C (2010) Image-based vehicle tracking and classification on the highway. In: Proceedings of the IEEE ICGCS, pp 666–670

  • Lai A, Yung NHC (1998) A fast and accurate scoreboard algorithm for estimating stationary backgrounds in an image sequence. In: Circuits and systems, 1998. ISCAS ’98. In: Proceedings of the 1998 IEEE international symposium on, vol 4, pp 241–244 (1998)

  • Lin SP, Chen YH, Wu BF (2006) A real-time multiple-vehicle detection and tracking system with prior occlusion detection and resolution, and prior queue detection and resolution. Proc Pattern Recognit 1:828–831

    Google Scholar 

  • Lipton A, Fujiyoshi H, Patil R (1998) Moving target classification and tracking from real-time video. In: Applications of computer vision, 1998. WACV ’98. In: Proceedings of the 4th IEEE workshop on, pp 8–14

  • López-Rubio E, Luque-Baena RM (2011) Stochastic approximation for background modelling. Comput Vis Image Underst 115(6):735–749

    Article  Google Scholar 

  • Luque R, Domínguez E, Palomo E, Muñoz J (2010) An art-type network approach for video object detection. In: European symposium on artificial neural networks, pp 423–428

  • Nagai A, Kuno Y, Shirai Y (1996) Surveillance system based on spatio-temporal information. In: Image processing, 1996. In: Proceedings of the international conference on, vol 1, pp 593–596

  • Na W, Tao W (2012) Proximal support vector machine based pavement image classification. In: IEEE 5th international conference on advanced computational intelligence (ICACI), pp 686–688

  • Padoan A Jr, De A Barreto G, Araújo A (2003) Modeling and production of robot trajectories using the temporal parametrized self organizing maps. Int J Neural Syst 13(2):119–127

    Article  Google Scholar 

  • Ridder C, Munkelt O, Kirchner H (1995) Adaptive background estimation and foreground detection using kalman-filtering. In: Proceedings of the international conference on recent advances in mechatronics, ICRAM, pp 193–199

  • Robert K (2009) Night-time traffic surveillance: a robust framework for multi-vehicle detection, classification and tracking. In: Proceedings of IEEE AVSS, pp 1–6

  • Soille P (2003) Morphol Image Anal: Princ Appl, 2nd edn. Springer, New York

    Google Scholar 

  • Stauffer C, Grimson W (2000) Learning patterns of activity using real time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747–767

    Article  Google Scholar 

  • Stauffer C, Grimson W (1999) Adaptive background mixture models for real-time tracking. In: IEEE computer society conference on Computer vision and pattern recognition, vol 2, pp 246–252

  • Van Deusen PC, Irwin LL (2012) A robust weighted EM algorithm for use–availability data. Environ Ecol Stat 19:205–217

  • Valero S, Chanussot J, Benediktsson J, Talbot H, Waske B (2010) Advanced directional mathematical morphology for the detection of the road network in very high resolution remote sensing images. Pattern Recognit Lett 31(10):1120–1127

  • Walter J, Ritter H (1996) Rapid learning with parametrized self-organizing maps. Neurocomputing 12(2–3):131–153

    Article  MATH  Google Scholar 

  • Yu X, Beucher S, Bilodeau M (1992) Road tracking, lane segmentation and obstacle recognition by mathematical morphology. In: Proceedings of the intelligent vehicles symposium, pp 166–172

  • Zingman I, Saupe D, Lambers K (2014) A morphological approach for distinguishing texture and individual features in images. Pattern Recognit Lett 47(0):129–138

  • Zivkovic Z, van der Heijden F (2006) Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognit Lett 27(7):773–780

    Article  Google Scholar 

  • Zulkifley MA, Moran B (2012) Robust hierarchical multiple hypothesis tracker for multiple-object tracking. Expert Syst Appl 39(16):12,319–12,331

    Article  Google Scholar 

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Acknowledgments

This work is partially supported by the Projects TIN2011-24141 from MEC-SPAIN and TIC-6213 and TIC-657 (Junta de Andalucía). Additionally, the authors acknowledge support through Grants TIN2010-16556 from MICINN-SPAIN and P08-TIC-04026 (Junta de Andalucía). All of them include FEDER funds.

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Authors and Affiliations

  1. Department of Computer Systems and Telematics Engineering, University of Extremadura, University Centre of Mérida, 06800, Mérida, Spain

    R. M. Luque-Baena

  2. Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35, 29071, Málaga, Spain

    Ezequiel López-Rubio, E. Domínguez, E. J. Palomo & J. M. Jerez

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  1. R. M. Luque-Baena
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  2. Ezequiel López-Rubio
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  3. E. Domínguez
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  4. E. J. Palomo
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  5. J. M. Jerez
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Correspondence to R. M. Luque-Baena.

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Communicated by I. R. Ruiz.

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Luque-Baena, R.M., López-Rubio, E., Domínguez, E. et al. A self-organizing map to improve vehicle detection in flow monitoring systems. Soft Comput 19, 2499–2509 (2015). https://doi.org/10.1007/s00500-014-1575-3

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