Imagine a scenario where you are given a picture of a crowd and are asked to estimate the number of people present in the image. A crowd counting model comes in handy in such a scenario. Before we go ahead and build a model to perform crowd counting, let's understand the data available and the model architecture first.
In order to train a model that predicts the number of people in an image, we will have to load the images first. The images should constitute the location of the center of the heads of all the people present in the image. A sample of the input image and the location of the center of the heads of the respective people in the image is as follows (source: ShanghaiTech dataset (https://github.com/desenzhou/ShanghaiTechDataset)):
In the preceding example, the image representing ground truth (the image on the right – the center of the heads of the people present in the image) is extremely sparse. There are exactly N white pixels, where N is the number...
