Self-driving technology and safety monitoring devices in intelligent transportation systems
require superb capacity for context awareness. Accurately inferring the counts of crowds and …

The task of crowd counting and localization is to predict the count and position of people in a
crowd, which is a practical and essential subtask in crowd analysis and smart city systems …

Abstract Currently, state-of-the-art crowd counting algorithms rely excessively on location-
level annotations, which are burdensome to acquire. When only weak supervisory signals at …

Object counting, defined as the task of accurately predicting the number of objects in static
images or videos, has recently attracted considerable interest. However, the unavoidable …

With the rapid advancement of deep learning, the performance of crowd counting has
improved significantly. Nonetheless, existing crowd counting models primarily depend on a …

The integration of remote sensing object counting in the Mobile Edge Computing (MEC)
environment is of crucial significance and practical value. However, the presence of …

Dense crowd counting has become an essential technology for urban security management.
The traditional crowd counting methods mainly apply to the scene with a single view and …

Crowd counting, a crucial computer vision task, aims at estimating the number of individuals
in various environments. Each person in crowd counting datasets is typically annotated by a …

Most of the existing remote sensing image super-resolution (SR) methods based on deep
learning tend to learn the mapping from low-resolution (LR) images to high-resolution (HR) …

Crowd counting has substantial practical applications in various consumer-oriented areas,
particularly for safety assessments and marketing strategies. However, considering the …