Drone-Based Car Counting via Density Map Learning

Abstract

Car counting on drone-based images is a challenging task in computer vision. Most advanced methods for counting are based on density maps. Usually, density maps are first generated by convolving ground truth point maps with a Gaussian kernel for later model learning (generation). Then, the counting network learns to predict density maps from input images (estimation). Most studies focus on the estimation problem while overlooking the generation problem. In this paper, a training framework is proposed to generate density maps by learning and train generation and estimation subnetworks jointly. Experiments demonstrate that our method outperforms other density map-based methods and shows the best performance on drone-based car counting.