Mask Guided GAN for Density Estimation and Crowd Counting

Abstract

Density estimation aims to predict the spatial distribution of a crowd scene, and crowd counting aims to automatically check the number of heads as close as the ground truth. We propose a mask guided GAN (Generative Adversarial Network) architecture to solve these two problems synthetically. Step one is generating a segmentation mask, separating the crowd region from the background and redundant information. Step two is predicting the density map with an adversarial learning process guided by the former mask information. Moreover, we branch out from the base network and feed into a counting regressor, which is solely to provide more accurate counting results. The whole scheme is trained collaboratively by compositing density loss (a weighted loss to balance the influence of different data) and counting loss (an MSE loss for counting regression branch). Through the mask information guidance, GAN likely finds its way training to capture more distinguishing features. Therefore, we also achieve more accurate prediction results. Experimental results on different datasets including collected from the internet and the actual scene in Shanghai, the second-most populous city located on China's southeast coast, indicate the validity and robustness by comparable counting numbers and high-quality density maps focusing on crowd area. INDEX TERMS Adversarial learning, mask guided network, density estimation, crowd counting.

Enhancement of Local Crowd Location and Count: Multiscale Counting Guided by Head RGB-Mask

“…For those tiny heads, efficient scale-adaptive perceptrons should be designed. Fortunately, RGB image and head RGB-Mask image feature fusion can provide a prior for estimating head size, which helps to set suitable scale fusion perceptrons for different scales of human heads [ 21 , 22 ].…”

Performance Comparison and Analysis for Large-Scale Crowd Counting Based on Convolutional Neural Networks

“…Different from previous approaches, density estimation techniques learn a mapping between local features and corresponding density maps [8]. The number of people is obtained by generating a density estimation map for each patch or image and then add the values of the density map of an image to produce the final count.…”