Dense Crowd Counting Convolutional Neural Networks with Minimal Data using Semi-Supervised Dual-Goal Generative Adversarial Networks

Abstract

In this work, we generalize semi-supervised generative adversarial networks (GANs) from classification problems to regression for use in dense crowd counting. In the last several years, the importance of improving the training of neural networks using semi-supervised training has been thoroughly demonstrated for classification problems. This work presents a dual-goal GAN which seeks both to provide the number of individuals in a densely crowded scene and distinguish between real and generated images.

Year of Publication
2019
Conference Name
IEEE Conference on Computer Vision and Pattern Recognition: Learning with Imperfect Data Workshop
Date Published
06