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

  • Sami BourouisEmail author
  • Yacine Laalaoui
  • Nizar Bouguila


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.


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



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.


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© 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

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