Human Detection & Counting from Topological View Image

Abstract

In this thesis we proposed a model called CSRNet provides data-driven and deep learning that can understand highly congested scenes. Two major components: a convolutional neural network (CNN) as the front-end for 2D feature extraction and a dilated CNN for the back-end, which uses dilated kernels to deliver larger reception fields and to replace pooling operations. CSRNet is an easy-trained model because of its pure convolutional structure. Used dataset for training and testing purpose is ShanghaiTech dataset. CSRNet is used as architecture for strong transfer learning ability and its flexible architecture for easily concatenating the back-end for density map generation. We choose VGG-16 as the front-end of CSRNet. Here the output size of this front-end network is 1/8 of the original input size. If we continue to stack more convolutional layers and pooling layers it is hard to generate high-quality density maps. It also adapts the Gaussian kernel to the average head size to blur all the annotations and the PSNR, SSIM to evaluate the quality of the output density map on ShanghaiTech Part A dataset. Which includes the density map resizing with interpolation and normalization for both ground truth and predicted density map. The perspectives of images are not fixed and the images are collected from very different scenarios. The Grid Average Mean Absolute Error is used for evaluation in this test and achieves a significant improvement on four different Grid Average Mean Absolute Error (GAME) crowd counting datasets with the state-of-the-art performance.