Most researchers focus on designing accurate crowd counting models with heavy
parameters and computations but ignore the resource burden during the model deployment …

Crowd counting is an application-oriented task and its inference efficiency is crucial for real-
world applications. However, most previous works relied on heavy backbone networks and …

Recently, density map regression-based methods have dominated in crowd counting owing
to their excellent fitting ability on density distribution. However, further improvement tends to …

One promising way to improve the performance of a small deep network is knowledge
distillation. Performances of smaller student models with fewer parameters and lower …

Crowd counting is challenging due to unconstrained imaging factors, eg, background
clutters, non-uniform distribution of people, large scale and perspective variations. Dealing …

One of appealing approaches to counting dense objects, such as crowd, is density map
estimation. Density maps, however, present ambiguous appearance cues in congested …

People in crowd scenes often exhibit the characteristic of imbalanced distribution. On the
one hand, people size varies largely due to the camera perspective. People far away from …

Crowd counting, ie, estimating the number of people in crowded areas, has attracted much
interest in the research community. Although many attempts have been reported, crowd …

Abstract Convolutional Neural Network (CNN) based methods generally take crowd
counting as a regression task by outputting crowd densities. They learn the mapping …

Crowd counting using deep convolutional neural networks (CNN) has achieved
encouraging progress in recent years. Nevertheless, how to efficiently address the problems …