Classifying and Counting Vehicles in Traffic Control Applications

  • Francesco Archetti
  • Enza Messina
  • Daniele Toscani
  • Leonardo Vanneschi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


This paper presents a machine learning system to handle traffic control applications. The input of the system is a set of image sequences coming from a fixed camera. The system can be divided into two main subsystems: the first one, based on Artificial Neural Networks classifies the typology of vehicles moving within a limited image area for each frame of the sequence; the second one, based on Genetic Algorithms, takes as input the frame-by-frame classifications and reconstructs the global traffic scenario by counting the number of vehicles of each typology. This task is particularly hard when the frame rate is low. The results obtained by our system are reliable even for very low frame rate (i.e. four frames per second). Our system is currently used by a company for real-time traffic control.


Genetic Algorithm Hide Markov Model Image Sequence Feed Forward Neural Network Probabilistic Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Francesco Archetti
    • 1
  • Enza Messina
    • 1
  • Daniele Toscani
    • 1
  • Leonardo Vanneschi
    • 1
  1. 1.Dipartimento di Informatica, Sistemistica e Comunicazione (D.I.S.Co.)University of Milano-BicoccaMilanItaly

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