The article is dedicated to the presentation of a vision-based system for road vehicles counting and classification. The system is able to achieve counting with a very good accuracy even in difficult scenarios linked to occlusions and/or presence of shadows. The principle of the system is to use already installed cameras in road networks without any additional calibration procedure. We propose a robust segmentation algorithm that detects foreground pixels corresponding to moving vehicles. First, the approach models each pixel of the background with an adaptive Gaussian distribution. This model is coupled with a motion detection procedure which allows to correctly locate in space and time moving vehicles. The nature of trials carried out, including peak periods and various vehicle types, leads to an increase of occlusions between cars and between cars and trucks. A specific method for severe occlusion detection, based on the notion of solidity, has been carried out, and tested. Furthermore, the method developed in this work is capable of managing the shadows with high resolution. The related algorithm has been tested and compared to a classical method. Experimental results based on four large data-sets show that our method can count and classify vehicles in real-time with a high level of performance (more than 98%) under different environmental situations, thus performing better than the conventional inductive loop detectors.
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