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.

Archetti, F., Messina, V., Toscani, D., Vanneschi, L. (2006). Classifying and counting vehicles in traffic control applications. In Applications of Evolutionary Computing EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC, Budapest, Hungary, April 10-12, 2006, Proceedings (pp.495-499). Springer Berlin, Heidelberg [10.1007/11732242_44].

Classifying and counting vehicles in traffic control applications

ARCHETTI, FRANCESCO ANTONIO;MESSINA, VINCENZINA;VANNESCHI, LEONARDO
2006

Abstract

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.
paper
Classification (of information); Genetic algorithms; Image processing; Neural networks; Real time systems; Traffic control
English
EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC - April 10-12, 2006
2006
Rothlauf, F; Branke, J; Cagnoni, S; Costa, E; Cotta, C; Drechsler, R; Lutton, E; Machado, P; Moore, JH; Romero, J; Smith, GD; Squillero, G; Takagi, H
Applications of Evolutionary Computing EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC, Budapest, Hungary, April 10-12, 2006, Proceedings
9783540332374
2006
3907 LNCS
495
499
none
Archetti, F., Messina, V., Toscani, D., Vanneschi, L. (2006). Classifying and counting vehicles in traffic control applications. In Applications of Evolutionary Computing EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC, Budapest, Hungary, April 10-12, 2006, Proceedings (pp.495-499). Springer Berlin, Heidelberg [10.1007/11732242_44].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/2913
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