Factors such as scale variation, occlusion and complex backgrounds make crowd number
estimation in crowded scenes a challenging task. To cope with the scale variation in crowd …

This article proposed a novel classification framework that can classify the samples of
multiple domains based on the outputs of multiple models. Different from the existing …

Crowd counting is a challenging and relevant computer vision task. Most of the existing
methods are image-based, ie, they only exploit the spatial information of a single image to …

We develop a Synthetic Fusion Pyramid Network (SPF-Net) with a scale-aware loss function
design for accurate crowd counting. Existing crowd-counting methods assume that the …

Crowd counting is critical for video surveillance and public safety. However, due to the
impact of perspective effects, large-scale variations have become one of the main …

Thisresearch work delves into the significance of people counting systems, emphasizing
their role in furnishing valuable data for operational improvement, security enhancement …

Although great progress has been made in crowd counting, accurate estimation of crowd
numbers in high-density areas and full mitigation of the interference of background noise …

This paper aims to develop a fast method that can estimate and extract the scenes with
crowds from untrimmed streaming videos for surveillance purpose. To this end, we have …

One of appealing approaches to guiding learnable parameter optimization, such as feature
maps, is global attention, which enlightens network intelligence at a fraction of the cost …

Two important tasks in crowd analysis are crowd counting and crowd localization. In this
paper, we introduce map-based crowd counting and localization methods, including density …