1. A method performed by a processor for detecting presence of a vehicle in one or more images, comprising: determining a region of interest (ROI) in the one or more images, without a priori knowledge of whether the vehicle is present in the ROI; computing a plurality of sets of image descriptors, each one of the sets of image descriptors corresponding to a location within the ROI; classifying each of the sets of descriptors, using a multi-class classifier comprising a structure of related classifiers for a set of associated vehicle parts, to obtain a likelihood value of whether a vehicle part is present at the location corresponding to the set of descriptors being classified; and determining whether the vehicle is likely present within the ROI based on a comparison between a function of the obtained likelihood values and a predetermined threshold value; wherein the multi-class classifier used to classify each of the sets of descriptors is trained by: selecting a plurality of training images containing vehicle models; semantically labeling regions in the training images corresponding to parts of the vehicle models; collecting a set of positive samples comprising image content inside of the labeled regions from the plurality of training images, and a set of negative samples comprising image content outside of the labeled regions from the plurality of training images; characterizing the samples so as to derive positive and negative feature vectors for the samples; and using the positive and negative feature vectors to train the multi-class classifier.