A low-cost vehicle counter for next-generation ITS

This paper describes a vehicle counter application to be used in low-cost and low-complexity devices to be deployed in next-generation pervasive Intelligent Transport Systems. The first part of the paper introduces the Linesensor theory, which exploits the temporal redundancy of the movement to enable the processing of a 1D images (i.e. lines), thus reducing the complexity for extracting features and understanding the environment. Because of the high speed of the objects to be detected, the proposed application requires a very high frame rate and consequently an optimised design for the whole computer vision pipeline. For these reasons, in the second part of the paper, we propose a low-complexity background modelling algorithm permitting to extract information related to the whole image from a single metric. Our arguments demonstrate that the proposed algorithm has comparable performance in the segmentation operation as other state-of-the-art techniques, but reducing significantly the computational cost.