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.
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Archetti, F., Messina, E., Toscani, D., Vanneschi, L. (2006). Classifying and Counting Vehicles in Traffic Control Applications. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_44
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DOI: https://doi.org/10.1007/11732242_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33237-4
Online ISBN: 978-3-540-33238-1
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