# HOG vs. neural networks for person detection

I am very new to computer vision, (a high school student) and I am working on a project to count the number of people present in a room. I have tried to use the HOGDecriptor for person detection provided by OpenCV (HOGDescriptor_getDefaultPeopleDetector()) but it is very inaccurate, understandably. I was thinking that it may be advantageous to train my own system, since each camera setup would be specific to a certain room. This would greatly restrict the bounds of what the computer has to recognize. (What I mean by this is that if I am analyzing a conference room, little about the environment changes, other than the addition of people. This would limit the scope of the detection problem.)

My question is whether to use a HOGDescriptor or a Neural Network. I was planning on using a HOGDescriptor but was advised that a Neural Network may be more accurate. At the same time however, the way I understand a Neural Network works is that I provide the number of input neurons as well as the number of output neurons, and the training algorithm creates the hidden neurons in between. The problem with this approach is that I would like to be able to count an arbitrary number of people in the camera frame, and thus I could not provide the training algorithm with a definite number of end neurons. I addition, I am able to mark frames positive or negative (whether or not they include people in them) quite easily, but providing the description of the number of people in them would be significantly harder. For both these reasons, it would seem to me that HOGDescriptors would work better, because they do not need to be told how many people are in the frame, and they can create an arbitrary number of "end neurons."

Any help clarifying the benefits of both technologies, as well as advice on which to use, would be greatly appreciated. I realize that this question might be very elementary, but I am willing to learn and will take all responses very seriously. Thank you.