Advertisement

Bayesian frameworks for traffic scenes monitoring via view-based 3D cars models recognition

  • Sami BourouisEmail author
  • Yacine Laalaoui
  • Nizar Bouguila
Article
  • 28 Downloads

Abstract

Traffic Scenes Monitoring has been a topic of large research in the last decade. An important step is the recognition of cars. Indeed, recognizing 3D models of cars could allow efficient tracking and detection. In this work we propose to develop new flexible and powerful nonparametric frameworks for the problem of data modeling and 3D recognition. In particular, we propose a Bayesian inference method via scaled Dirichlet mixture models. The consideration of scaled Dirichlet mixture is encouraged by its flexibility recently obtained in several real-life applications. Moreover, the consideration of Bayesian learning is attractive in several ways. It makes it possible to take uncertainty into account by introducing prior information on the parameters, it permits to overcome learning issues regarding the under and/or over-fitting. and it permits simultaneous parameters estimation and model selection. We investigate in this work the integration of both Markov Chain Monte Carlo (MCMC) and reversible jump MCMC (RJMCMC) techniques for learning the resulting models. Detailed experiments have been conducted to demonstrate the advantages of our Bayesian frameworks.

Keywords

Finite and infinite mixture models Bayesian framework Scaled Dirichlet Markov Chain Monte Carlo (MCMC) Reversible jump MCMC (RJMCMC) Traffic scenes monitoring 

Notes

Acknowledgment

We want to thank the Deanship of Scientific Research at Taif University, KSA, for their support under grant 1-437-5046. We want to thank also the associate editor and all reviewers.

References

  1. 1.
    Aitchison J (2003) The statistical Analysis of Compositional Data. The Blackburn Press, CaldwellzbMATHGoogle Scholar
  2. 2.
    Amayri O, Bouguila N (2013) On online high-dimensional spherical data clustering and feature selection. Eng Appl AI 26(4):1386–1398Google Scholar
  3. 3.
    Amayri O, Bouguila N (2016) RJMCMC Learning for clustering and feature selection of l2-normalized vectors. In: International conference on control, decision and information technologies, coDIT 2016, saint julian’s, malta, april 6-8, 2016, pp 269–274Google Scholar
  4. 4.
    Bertrand A, Al-osaimi FR, Bouguila N (2016) View-based 3d objects recognition with expectation propagation learning. In: Advances in visual computing - 12th international symposium, ISVC 2016, las vegas, NV, USA, December 12-14, 2016, Proceedings, Part II, pp 359–369Google Scholar
  5. 5.
    Beymer D, McLauchlan P, Coifman B, Malik J (1997) A real-time computer vision system for measuring traffic parameters. In: 1997. Proceedings., 1997 IEEE computer society conference on computer vision and pattern recognition, IEEE, pp 495–501Google Scholar
  6. 6.
    Bouguila N, Wang JH, Hamza AB (2010) Software modules categorization through likelihood and bayesian analysis of finite dirichlet mixtures. J Appl Stat 37 (2):235–252MathSciNetCrossRefGoogle Scholar
  7. 7.
    Bouguila N, Ziou D (2008) A dirichlet process mixture of dirichlet distributions for classification and prediction. In: Proc of the IEEE workshop on machine learning for signal processing (MLSP), pp 297–302Google Scholar
  8. 8.
    Bouguila N, Ziou D (2008) A dirichlet process mixture of dirichlet distributions for classification and prediction. In: 2008 IEEE workshop on machine learning for signal processing, pp 297–302Google Scholar
  9. 9.
    Bouguila N, Elguebaly T (2012) A fully bayesian model based on reversible jump MCMC and finite beta mixtures for clustering. Expert Syst Appl 39(5):5946–5959CrossRefGoogle Scholar
  10. 10.
    Bouguila N, Ziou D (2005) Mml-based approach for finite dirichlet mixture estimation and selection Perner, p., imiya, a. (eds.) machine learning and data mining in pattern recognition, 4th international conference, MLDM 2005, leipzig, germany, july 9-11, 2005, proceedings. Lecture notes in computer science, vol. 3587, pp. 42–51. SpringerGoogle Scholar
  11. 11.
    Bouguila N, Ziou D (2005) On fitting finite dirichlet mixture using ECM and MML. In: Wang P, Singh M, Apté C, Perner P (eds) Pattern recognition and data mining, Third international conference on advances in pattern recognition, ICAPR 2005, Bath, UK, August 22-25, 2005, Proceedings, Part I. Lecture Notes in Computer Science, vol 3686. Springer, pp 172–182Google Scholar
  12. 12.
    Bouguila N, Ziou D (2005) A probabilistic approach for shadows modeling and detection. In: Proceedings of the 2005 international conference on image processing, ICIP 2005, Genoa, Italy, September 11-14, 2005, pp 329–332Google Scholar
  13. 13.
    Bouguila N, Ziou D (2006) Unsupervised selection of a finite dirichlet mixture model: an mml-based approach. IEEE Trans Knowl Data Eng 18(8):993–1009CrossRefGoogle Scholar
  14. 14.
    Bouguila N, Ziou D (2010) A dirichlet process mixture of generalized dirichlet distributions for proportional data modeling. IEEE Trans Neural Networks 21(1):107–122CrossRefGoogle Scholar
  15. 15.
    Bouguila N, Ziou D, Hammoud RI (2009) On bayesian analysis of a finite generalized dirichlet mixture via a metropolis-within-gibbs sampling. Pattern Anal Appl 12(2):151–166MathSciNetCrossRefGoogle Scholar
  16. 16.
    Bouguila N, Ziou D, Vaillancourt J (2004) Unsupervised learning of a finite mixture model based on the dirichlet distribution and its application. IEEE Trans Image Processing 13(11):1533–1543CrossRefGoogle Scholar
  17. 17.
    Bourouis S, Laalaoui Y, Bouguila N (2018) A purely bayesian approach for proportional visual data modelling. IJIEI 6(5):491–508CrossRefGoogle Scholar
  18. 18.
    Channoufi I, Bourouis S, Bouguila N, Hamrouni K (2018) Color image segmentation with bounded generalized gaussian mixture model and feature selection. In: 2018 4th international conference on advanced technologies for signal and image processing (ATSIP), IEEE, pp 1–6Google Scholar
  19. 19.
    Channoufi I, Bourouis S, Bouguila N, Hamrouni K (2018) Image and video denoising by combining unsupervised bounded generalized gaussian mixture modeling and spatial information. Multimedia Tools and Applications, 1–16Google Scholar
  20. 20.
    Cho Y, Rice J (2006) Estimating velocity fields on a freeway from low-resolution videos. IEEE Trans Intell Transp Syst 7(4):463–469CrossRefGoogle Scholar
  21. 21.
    Csurka G, Dance CR, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision, 8th european conference on computer vision (ECCV), pp 1–22Google Scholar
  22. 22.
    Elguebaly T, Bouguila N (2013) Simultaneous bayesian clustering and feature selection using rjmcmc-based learning of finite generalized dirichlet mixture models. Signal Process 93(6):1531–1546CrossRefGoogle Scholar
  23. 23.
    Fan W, Bouguila N (2013) Online learning of a dirichlet process mixture of beta-liouville distributions via variational inference. IEEE Trans Neural Netw Learning Syst 24(11):1850–1862CrossRefGoogle Scholar
  24. 24.
    Fan W, Bouguila N (2013) Variational learning of a dirichlet process of generalized dirichlet distributions for simultaneous clustering and feature selection. Pattern Recogn 46(10):2754–2769CrossRefGoogle Scholar
  25. 25.
    Fan W, Bouguila N, Ziou D (2011) Unsupervised anomaly intrusion detection via localized bayesian feature selection. In: 11Th IEEE international conference on data mining, ICDM 2011, vancouver, BC, Canada, December 11-14, 2011, pp 1032–1037Google Scholar
  26. 26.
    Fan W, Bouguila N, Ziou D (2011) A variational statistical framework for object detection. In: Lu B, Zhang L, Kwok JT (eds) Neural information processing - 18th international conference, ICONIP 2011, shanghai, china, november 13-17, 2011, proceedings, Part II. Lecture notes in computer science, vol 7063. Springer, pp 276–283Google Scholar
  27. 27.
    Fan W, Sallay H, Bouguila N, Bourouis S (2014) A hierarchical infinite generalized dirichlet mixture model with feature selection. In: Bouchachia A (ed) Adaptive and intelligent systems - Third international conference, ICAIS 2014, Bournemouth, UK, September 8-10, 2014. Proceedings. Lecture Notes in Computer Science, vol 8779. Springer, pp 1–10.  https://doi.org/10.1007/978-3-319-11298-5_1
  28. 28.
    Fan W, Sallay H, Bouguila N, Bourouis S (2015) A hierarchical dirichlet process mixture of generalized dirichlet distributions for feature selection. Comput Electr Eng 43:48–65CrossRefGoogle Scholar
  29. 29.
    Ferguson TS (1973) A bayesian analysis of some nonparametric problems. Ann Stat 1(2):209–230MathSciNetCrossRefGoogle Scholar
  30. 30.
    Gelfand A, Kottas A (2002) A computational approach for full nonparametric bayesian inference under dirichlet process mixture models. J Comput Graph Stat 11:289–305MathSciNetCrossRefGoogle Scholar
  31. 31.
    Hofmann T (2001) Unsupervised learning by probabilistic latent semantic analysis. Mach Learn 42(1/2):177–196CrossRefGoogle Scholar
  32. 32.
    Jain S, Neal RM (2004) A split-merge markov chain monte carlo procedure for the dirichlet process mixture model. J Comput Graph Stat 13:158–182MathSciNetCrossRefGoogle Scholar
  33. 33.
    Karavasilis V, Nikou C, Likas A (2015) Visual tracking using spatially weighted likelihood of gaussian mixtures. Comput Vis Image Underst 140:43–57CrossRefGoogle Scholar
  34. 34.
    Khraief C, Bourouis S, Hamrouni K (2012) Unsupervised video objects detection and tracking using region based level-set. In: 2012 international conference on multimedia computing and systems (ICMCS), IEEE, pp 201–206Google Scholar
  35. 35.
    Kim S, Lewis ME, White CC (2005) Optimal vehicle routing with real-time traffic information. IEEE Trans Intell Transp Syst 6(2):178–188CrossRefGoogle Scholar
  36. 36.
    Lan X, Ma AJ, Yuen PC (2014) Multi-cue visual tracking using robust feature-level fusion based on joint sparse representation. In: 2014 IEEE Conference on computer vision and pattern recognition, pp 1194–1201Google Scholar
  37. 37.
    Lan X, Ma AJ, Yuen PC, Chellappa R (2015) Joint sparse representation and robust feature-level fusion for multi-cue visual tracking. IEEE Trans Image Process 24(12):5826–5841MathSciNetCrossRefGoogle Scholar
  38. 38.
    Lan X, Zhang S, Yuen PC, Chellappa R (2018) Learning common and feature-specific patterns: a novel multiple-sparse-representation-based tracker. IEEE Trans Image Process 27(4):2022–2037MathSciNetCrossRefGoogle Scholar
  39. 39.
    Lan X, Ye M, Zhang S, Zhou H, Yuen PC (2018) Modality-correlation-aware sparse representation for rgb-infrared object tracking. Pattern Recognition LettersGoogle Scholar
  40. 40.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110MathSciNetCrossRefGoogle Scholar
  41. 41.
    MacEachern SN, Müller P (1998) Estimating mixture of dirichlet process models. J Comput Graph Stat 7:223–238Google Scholar
  42. 42.
    McLachlan GJ, Peel D (2000) Finite mixture models. Wiley, New YorkCrossRefGoogle Scholar
  43. 43.
    Najar F, Bourouis S, Bouguila N, Belghith S (2017) A comparison between different gaussian-based mixture models. In: 2017 IEEE/ACS 14th international conference on computer systems and applications (AICCSA), IEEE, pp 704–708Google Scholar
  44. 44.
    Najar F, Bourouis S, Bouguila N, Belghith S (2018) A fixed-point estimation algorithm for learning the multivariate ggmm: application to human action recognition. In: 2018 IEEE Canadian conference on electrical & computer engineering (CCECE), IEEE, pp 1–4Google Scholar
  45. 45.
    Oboh BS, Bouguila N (2017) Unsupervised learning of finite mixtures using scaled dirichlet distribution and its application to software modules categorization. In: 2017 IEEE international conference on industrial technology (ICIT), IEEE, pp 1085–1090Google Scholar
  46. 46.
    Raftery AE, Lewis SM (1992) One long run with diagnostics: Implementation startegies for markov chain monte carlo. Stat Sci 7(4):493–497CrossRefGoogle Scholar
  47. 47.
    Rasmussen CE (2000) The infinite gaussian mixture model. In: Advances in neural information processing systems (NIPS), pp 554–560Google Scholar
  48. 48.
    Richardson S, Green PJ (1997) On bayesian analysis of mixtures with an unknown number of components (with discussion). J R Stat Soc Ser B 59(4):731–792CrossRefGoogle Scholar
  49. 49.
    Robert CP, Casella G (1999) Monte Carlo Statistical Methods. Springer, BerlinCrossRefGoogle Scholar
  50. 50.
    Schoepflin TN, Dailey DJ (2003) Dynamic camera calibration of roadside traffic management cameras for vehicle speed estimation. IEEE Trans Intell Transp Syst 4 (2):90–98CrossRefGoogle Scholar
  51. 51.
    Shastry AC, Schowengerdt RA (2005) Airborne video registration and traffic-flow parameter estimation. IEEE Trans Intell Transp Syst 6(4):391–405CrossRefGoogle Scholar
  52. 52.
    Song KT, Tai JC (2006) Dynamic calibration of pan–tilt–zoom cameras for traffic monitoring. IEEE Trans Syst Man Cybern B Cybern 36(5):1091–1103CrossRefGoogle Scholar
  53. 53.
    Sun S, Zhang C, Yu G (2006) A bayesian network approach to traffic flow forecasting. IEEE Trans Intell Transp Syst 7(1):124–132CrossRefGoogle Scholar
  54. 54.
    Veeraraghavan H, Masoud O, Papanikolopoulos NP (2003) Computer vision algorithms for intersection monitoring. IEEE Trans Intell Transp Syst 4(2):78–89CrossRefGoogle Scholar
  55. 55.
    Wang M, Gao Y, Lu K, Rui Y (2013) View-based discriminative probabilistic modeling for 3d object retrieval and recognition. IEEE Trans Image Process 22(4):1395–1407MathSciNetCrossRefGoogle Scholar
  56. 56.
    Weil R, Wootton J, Garcia-Ortiz A (1998) Traffic incident detection: sensors and algorithms. Math Comput Model 27(9-11):257–291CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Information Technology, College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia
  2. 2.The Concordia Institute for Information Systems Engineering (CIISE)Concordia UniversityMontrealCanada

Personalised recommendations