This paper presents a novel method to count people forvideo surveillance applications. Methods in the literatureeither follow a direct approach, by first detecting people andthen counting them, or an indirect approach, by establishinga relation between some easily detectable scene featuresand the estimated number of people. The indirect approachis considerably more robust, but it is not easy to take intoaccount such factors as perspective or people groups withdifferent densities.The proposed technique, while based on the indirect approach,specifically addresses these problems; furthermoreit is based on a trainable estimator that does not requirean explicit formulation of a priori knowledge about the perspectiveand density effects present in the scene at hand.In the experimental evaluation, the method has beenextensively compared with the algorithm by Albiol et al.,which provided the highest performance at the PETS 2009contest on people counting. The experimentation has usedthe public PETS 2009 datasets. The results confirm that theproposed method improves the accuracy, while retaining therobustness of the indirect approach.