Dense Crowd Counting Convolutional Neural Networks with Minimal Data using Semi-Supervised Dual-Goal Generative Adversarial Networks
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. This method allows the dual-goal GAN to benefit from unlabeled data in the training process, improving the predictive capabilities of the discriminating network compared to the fully-supervised version of the network. Typical semi-supervised GANs are unable to function in the regression regime due to biases introduced when using a single prediction goal. Using the proposed approach, the amount of data which needs to be annotated for dense crowd counting can be significantly reduced.