  To learn a reliable people counter from crowd images, head center annotations
are normally required. Annotating head centers is however a laborious and
tedious process in dense crowds. In this paper, we present an active learning
framework which enables accurate crowd counting with limited supervision: given
a small labeling budget, instead of randomly selecting images to annotate, we
first introduce an active labeling strategy to annotate the most informative
images in the dataset and learn the counting model upon them. The process is
repeated such that in every cycle we select the samples that are diverse in
crowd density and dissimilar to previous selections. In the last cycle when the
labeling budget is met, the large amount of unlabeled data are also utilized: a
distribution classifier is introduced to align the labeled data with unlabeled
data; furthermore, we propose to mix up the distribution labels and latent
representations of data in the network to particularly improve the distribution
alignment in-between training samples. We follow the popular density estimation
pipeline for crowd counting. Extensive experiments are conducted on standard
benchmarks i.e. ShanghaiTech, UCF CC 50, MAll, TRANCOS, and DCC. By annotating
limited number of images (e.g. 10% of the dataset), our method reaches levels
of performance not far from the state of the art which utilize full annotations
of the dataset.
