CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

We propose a network for Congested Scene Recognition called CSRNet to provide a data-driven and deep learning method that can understand highly congested scenes and perform accurate count estimation as well as present high-quality density maps. The proposed CSRNet is composed of 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. We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF CC 50 dataset, the WorldEXPO' 10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance. In the ShanghaiTech Part B dataset, CSRNet achieves 47.3% lower Mean Absolute Error (MAE) than the previous state-of-theart method. We extend the targeted applications for counting other objects, such as the vehicle in TRANCOS dataset. Results show that CSRNet significantly improves the output quality with 15.4% lower MAE than the previous state-of-the- art approach.