Generating high quality crowd density map based on perceptual loss

Abstract

High quality crowd density maps preserve a large amount of spatial information of crowd distribution, which provides significant priori information for the field of crowd behavior analysis and anomaly detection. Recent work on crowd density estimation pays more attention to the accuracy of crowd counting, ignoring the quality of crowd density map estimation. Hence, in this paper, we propose an end-to-end crowd density estimation network to generate high quality crowd density map. The original pixel-level Euclidean distance loss function in the Multi-column Convolutional Neural Network (MCNN) is replaced by the perceptual loss network. By optimizing the perceptual loss function that is defined as the differences between high-level semantic features generated by a pre-trained network, high-quality map estimation can be obtained. At the same time the accuracy of crowd counting and the sensitivity to the external environment can be improved. Extensive experiments conducted on challenging datasets validate the proposed method outperforms the state-of-the-art methods in both the crowd counting accuracy and the density estimation quality.