Crowd density estimation for fisheye images

Aiming at the problem that the traditional crowd density estimation methods are not applicable under the distortion of fisheye images, this paper presents a crowd density estimation method for fisheye images, which realizes the monitoring of human traffic in scene of using fisheye lens. For model structure, we introduced deformable convolution to improve the adaptability of the model to fisheye distortion. For generating the training targets, we used Gaussian transform to perform a distribution match on the density maps of annotations, which depends on the features of fisheye distortion. For training, we optimized the loss function to avoid the model from falling into local optimal solutions. In addition, we collected and labeled the corresponding dataset due to the lack of dataset for fisheye crowd estimation. At last, by comparing the subjective and objective experiments with classical algorithms, we proved the superiority of the crowd estimation method for fisheye images in this paper with the mean absolute error of 3.78 in the test dataset, which is lower than others. © 2022 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.