Yes. The general task of looking at arbitrary images and labeling objects (from a set of known categories) in those images is called "detection." In fact the pr

Yes. The general task of looking at arbitrary images and labeling objects (from a set of known categories) in those images is called "detection." In fact the problem you described is easier, because there's only one category (people), and the system only needs to provide a count, rather than provide bounding box rectangles around each object (which is what the standard "detection" task entails). Convolutional neural nets are the state of the art for this, specifically deep residual learning (http://arxiv.org/abs/1512.03385 http://arxiv.org/abs/1512.03385). It requires a good deal of background to understand what's going on and tune/implement the models, though, even if you just use the frameworks already out there. You probably don't even need that much data - you can probably grab pre-trained models and train them on a small additional dataset you collect. They can definitely handle arbitrary backgrounds, although having a standard background makes the problem even easier, again. Most deep learning computer vision algos are trained on 256x256 images, so having even larger images is just fine (you can downsample, or maybe even add up the predictions of different crops).